{
 "metadata": {
  "name": "",
  "signature": "sha256:27a83edb7926554471d87d020c9e2ca09c17cd35c0eabd1d18813bf1ef3a3c3f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Project\n",
      "CSE 802 - Pattern Recognition and Analysis Instructor: Dr. Arun Ross  \n",
      "Due Date: May 2, 5:00pm  \n",
      "\n",
      "\n",
      "Sebastian Raschka  \n",
      "Last updated: 04/24/2014\n",
      "<hr>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Sections\n",
      "- 1)\n",
      "    - Description of task 1\n",
      "    - About the dataset\n",
      "    - Reading in and analyzing the dataset\n",
      "    - Dividing the dataset radomly in training (70%) and test (30%) sets\n",
      "    - Visualizing the 3 classes in a scatter plot\n",
      "    - 1 a)\n",
      "        - Description of task 1 a)\n",
      "        - Discriminant Functions\n",
      "        - Decision Boundaries\n",
      "        - Implementing the Discriminant Function for arbitrary covariance matrices\n",
      "        - Implementing the descision rule\n",
      "        - Classifying data and calculating the empirical error\n",
      "            - Empirical error of the training dataset\n",
      "            - Empirical error of the test dataset\n",
      "    - 1 b)\n",
      "        - Description of task 1 b)\n",
      "        - About the Maximum Likelihood Estimate (MLE)\n",
      "        - MLE of the mean vector $\\pmb\\mu$\n",
      "        - MLE of the covariance matrix $\\pmb\\Sigma$\n",
      "        - Error on the test set using the estimated parameters\n",
      "    - 1 c)\n",
      "        - Description of task 1 c)\n",
      "        - Implementing the classifier using  Bayes' decision rule\n",
      "        - Error on the test set using the likelihoods from the Gaussian kernel estimate of the Parzen-window technique](#error_parzen)\n",
      "        - Using the Parzen-window technique for Gaussian kernel desnity estimation\n",
      "    - 1 d)\n",
      "        - Description of task 1 d)\n",
      "        - About the k-Nearest Neighbor Technique\n",
      "        - Implementing the code for finding the 1-Nearest Neighbor\n",
      "    - 1 e)\n",
      "        - Description of task 1 e)\n",
      "        - Creating training sets with variable size\n",
      "        - MLE error rate for varying training set sizes\n",
      "        - Error rate of kernel density estimaton via Parzen-window for varying training sizes\n",
      "        - Error rate of the k-nearest-neighbor technique for varying training sizes\n",
      "        - Plotting the error rate as a function of the size of the training set for each of the 3 cases\n",
      "    - 1 f) - Discussion\n",
      "        - 1 f) i. - Best Classifier\n",
      "        - 1 f) ii. - Performance changes as function of patterns\n",
      "        - 1 f) iii. - Performance changes and the number of data patterns\n",
      "        - 1 f) iv. Number of training patterns per class\n",
      "-  2 - Thoughts on a Ray Kurzweil quote"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"task_1\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"descr_task_1\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "\n",
      "### Description of Task 1): \n",
      "[65 points] Consider the dataset available here: [project_data.txt](./data/project_data.txt).  \n",
      "It consists of two-dimensional patterns, $\\pmb{x} = [x_1, x_2]^t$ , pertaining to  \n",
      "3 classes $(\\omega_1,\\omega_2,\\omega_3)$. The feature values are indicated in the first two  \n",
      "columns while the class labels are specified in the last column. The priors  \n",
      "of all 3 classes are the same. Randomly partition this dataset into a training set  \n",
      "(70% of each class) and a test set (30% of each class)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"about_data\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### About the dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data set consists of 1500 rows and 3 columns, where  \n",
      "- column 1: $x_1$ values  \n",
      "- column 2: $x_2$ values  \n",
      "- column 3: class labels  \n",
      "\n",
      "**Excerpt from the dataset: **   \n",
      "<code>    1.8569 -3.4702 1  \n",
      "    -0.2096 -2.8342 1  \n",
      "    -1.0265 2.1614 1  \n",
      "    [...]  \n",
      "    9.3851 4.0336 2  \n",
      "    10.1375 1.1495 2  \n",
      "    11.7569 0.8005 2  \n",
      "    [...]  \n",
      "    3.9854 5.1360 3  \n",
      "    2.7592 5.9536 3  \n",
      "    4.1379 4.3258 3  \n",
      "    [...]  </code>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"reading_data\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Reading in and analyzing the dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "np.random.seed(1234568)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_projdata = np.genfromtxt('./data/project_data.txt', delimiter=' ')\n",
      "\n",
      "# Test if the data is read in in the correct dimensions:\n",
      "assert(all_projdata.shape == (1500, 3))\n",
      "for c_label in range(1,4): # class labels 1-3 are in column 3\n",
      "    assert(all_projdata[all_projdata[:,2] == c_label].shape == (500,3))\n",
      "\n",
      "# Print basic statistics:\n",
      "for column in range(2):\n",
      "    print(50 * '-')\n",
      "    print(\"range of x_{}: ({}, {})\".format(column+1, \n",
      "                min(all_projdata[:,column]), max(all_projdata[:,0])))\n",
      "    print(\"mean of x_{}: {}\".format(column+1, \n",
      "                np.mean(all_projdata[:,column])))\n",
      "    print(\"standard_deviation of x_{}: {}\".format(\n",
      "            column+1, np.std(all_projdata[:,column])))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--------------------------------------------------\n",
        "range of x_1: (-6.8114, 17.4559)\n",
        "mean of x_1: 5.027464333333326\n",
        "standard_deviation of x_1: 4.574803380930654\n",
        "--------------------------------------------------\n",
        "range of x_2: (-7.9943, 17.4559)\n",
        "mean of x_2: 1.6648250000000002\n",
        "standard_deviation of x_2: 3.156841470283494\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name=\"divide_data\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Dividing the dataset radomly in training (70%) and test (30%) sets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Accomodation, since numpy arrays cannot be empty\n",
      "test_set = np.zeros(shape=(1,3))\n",
      "train_set = np.zeros(shape=(1,3))\n",
      "\n",
      "\n",
      "for i in range(0,3):\n",
      "    cl_shuffle = np.copy(all_projdata[all_projdata[:,2] == i+1])\n",
      "    np.random.shuffle(cl_shuffle)\n",
      "    test_set = np.append(test_set, \n",
      "                         cl_shuffle[0:150,:], axis=0)\n",
      "    train_set = np.append(train_set, \n",
      "                          cl_shuffle[150:,:], axis=0)\n",
      "                                  \n",
      "# delete the first placeholder \n",
      "# row used for initializing the array                                  \n",
      "test_set = np.delete(test_set, 0, axis=0)\n",
      "train_set = np.delete(train_set, 0, axis=0)\n",
      "\n",
      "assert(test_set.shape == (1500*0.3,3))\n",
      "assert(train_set.shape == (1500*0.7,3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"viz_div_data\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Visualizing the 3 classes in a scatter plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%pylab inline\n",
      "\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "# Plotting training and test datasets\n",
      "for dset,title in zip((test_set, train_set), ['Test', 'Training']):\n",
      "    f, ax = plt.subplots(figsize=(7, 7))\n",
      "    ax.scatter(dset[dset[:,2] == 1][:,0], \n",
      "            dset[dset[:,2] == 1][:,1], \n",
      "            marker='o', color='green', s=40, alpha=0.5, label='$\\omega_1$')\n",
      "    ax.scatter(dset[dset[:,2] == 2][:,0], \n",
      "               dset[dset[:,2] == 2][:,1],\n",
      "               marker='^', color='red', s=40, alpha=0.5, label='$\\omega_2$')\n",
      "    ax.scatter(dset[dset[:,2] == 3][:,0], \n",
      "               dset[dset[:,2] == 3][:,1], \n",
      "               marker='s', color='blue', s=40, alpha=0.5, label='$\\omega_3$')\n",
      "    plt.legend(loc='upper right') \n",
      "    plt.title('{} Dataset'.format(title), size=20)\n",
      "    plt.ylabel('$x_2$', size=20)\n",
      "    plt.xlabel('$x_1$', size=20)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAHPCAYAAAA4ZiFsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107159c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAHPCAYAAAA4ZiFsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1041454e0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"task_1a\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1a)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "<a name=\"descr_task_1a\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Description\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Let  \n",
      "\n",
      "$p([x_1, x_2]^t |\\omega_1) \u223c N([0,0]^t,4I), \\\\\n",
      "p([x_1, x_2]^t |\\omega_2) \u223c N([10,0]^t,4I), \\\\\n",
      "p([x_1, x_2]^t |\\omega_3) \u223c N([5,5]^t,5I),$\n",
      "\n",
      "where $I$ is the $2 \\times 2$ identity matrix.  \n",
      "What is the error rate on the test set when the  \n",
      "Bayesian decision rule is employed for classification?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bayes' Rule:\n",
      "\n",
      "\n",
      "$ P(\\omega_j|x) = \\frac{p(x|\\omega_j) * P(\\omega_j)}{p(x)}$ \n",
      "\n",
      "with **prior probabilities**: $P(\\omega_1) \\; = P(\\omega_2) \\; = P(\\omega_3)\\; = \\frac{1}{3}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"discr_func\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Discriminant Functions:\n",
      "\n",
      "The **objective function** is to maximize the discriminant function,  \n",
      "which we define as the posterior probability here to perform  \n",
      "a **minimum-error classification** (Bayes classifier).\n",
      "\n",
      "$ g_1(\\pmb x) = P(\\omega_1 | \\; \\pmb{x}), \\quad  g_2(\\pmb{x}) = P(\\omega_2 | \\; \\pmb{x}), \\quad  g_3(\\pmb{x}) = P(\\omega_2 | \\; \\pmb{x})$\n",
      "\n",
      "$ \\Rightarrow g_1(\\pmb{x}) = P(\\pmb{x}\\;|\\;\\omega_1) \\;\\cdot\\; P(\\omega_1) \\quad | \\; ln \\\\\n",
      "\\quad g_2(\\pmb x) = P(\\pmb{x}\\;|\\;\\omega_2) \\;\\cdot\\; P(\\omega_2) \\quad | \\; ln \\\\\n",
      "\\quad g_3(\\pmb x) = P(\\pmb{x}\\;|\\;\\omega_3) \\;\\cdot\\; P(\\omega_3) \\quad | \\; ln$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can drop the prior probabilities (since we have equal priors in this case): \n",
      "\n",
      "$ \\Rightarrow g_1(\\pmb{x}) = ln(P(\\pmb{x}\\;|\\;\\omega_1))  \\\\\n",
      "\\quad g_2(\\pmb{x}) = ln(P(\\pmb{x}\\;|\\;\\omega_2)) \\\\\n",
      "\\quad g_3(\\pmb{x}) = ln(P(\\pmb{x}\\;|\\;\\omega_3))$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The equations for the general multivariate normal case  \n",
      "with arbitrary covariance matrices  \n",
      "(i.e., different covariance matrices for each case):"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\Rightarrow g_1(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_1^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_1^{-1} \\pmb{\\mu}_{\\,1}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,1}^{\\,t}  \\Sigma_{1}^{-1} \\pmb{\\mu}_{\\,1} -\\frac{1}{2} ln(|\\Sigma_1|)\\bigg)$\n",
      "\n",
      "$g_2(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_2^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_2^{-1} \\pmb{\\mu}_{\\,2}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,2}^{\\,t} \\Sigma_{2}^{-1} \\pmb{\\mu}_{\\,2} -\\frac{1}{2} ln(|\\Sigma_2|)\\bigg)$\n",
      "\n",
      "$g_3(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_3^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_3^{-1} \\pmb{\\mu}_{\\,3}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,3}^{\\,t} \\Sigma_{3}^{-1} \\pmb{\\mu}_{\\,3} -\\frac{1}{2} ln(|\\Sigma_3|)\\bigg)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "**Let:**\n",
      "\n",
      "$\\pmb{W}_{i} = - \\frac{1}{2} \\Sigma_i^{-1}\\\\\n",
      "\\pmb{w}_i = \\Sigma_i^{-1} \\pmb{\\mu}_{\\,i}\\\\\n",
      "\\omega_{i0} = \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,i}^{\\,t}\\; \\Sigma_{i}^{-1} \\pmb{\\mu}_{\\,i} -\\frac{1}{2} ln(|\\Sigma_i|)\\bigg)$\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{W}_{1} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/8) & 0\\\\\n",
      "0 & -(1/8) \\\\\n",
      "\\end{array} \\bigg] $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{w}_{1} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/4) & 0\\\\\n",
      "0 & (1/4) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "0 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg] = \\bigg[ \n",
      "\\begin{array}{c}\n",
      "0 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg]$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\omega_{10} = -\\frac{1}{2} [0 \\quad 0 ] \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/4) & 0\\\\\n",
      "0 & (1/4) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "0 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg] \n",
      "- ln(4) = -ln(4)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "with $ \\quad g_1(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_1^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_1^{-1} \\pmb{\\mu}_{\\,1}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,1}^{\\,t}  \\Sigma_{1}^{-1} \\pmb{\\mu}_{\\,1} -\\frac{1}{2} ln(|\\Sigma_1|)\\bigg) $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\Rightarrow g_1(\\pmb{x}) = \\pmb{x}^{t}  \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/8) & 0\\\\\n",
      "0 & -(1/8) \\\\\n",
      "\\end{array} \\bigg] \\pmb{x} + [0 \\quad 0 ] \\; \\pmb x - ln(4) \n",
      "= \\pmb{x}^{t} (-\\frac{1}{8} \\; \\pmb{x}) - 2ln(2) $\n",
      "\n",
      "$ \\Rightarrow g_1(\\pmb{x}) = - \\frac{1}{8} (x^{2}_1 + x^{2}_2 ) - 2ln(2)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{W}_{2} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/8) & 0\\\\\n",
      "0 & -(1/8) \\\\\n",
      "\\end{array} \\bigg] $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{w}_{2} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/4) & 0\\\\\n",
      "0 & (1/4) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "10 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg] = \\bigg[ \n",
      "\\begin{array}{c}\n",
      "2.5 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg]$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\omega_{20} = -\\frac{1}{2} [10 \\quad 0 ] \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/4) & 0\\\\\n",
      "0 & (1/4) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "10 \\\\\n",
      "0 \\\\\n",
      "\\end{array} \\bigg] \n",
      "- ln(4) = -12.5-ln(4)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "with $ \\quad g_2(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_2^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_2^{-1} \\pmb{\\mu}_{\\,2}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,2}^{\\,t} \\Sigma_{2}^{-1} \\pmb{\\mu}_{\\,2} -\\frac{1}{2} ln(|\\Sigma_2|)\\bigg) $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\Rightarrow g_2(\\pmb{x}) = \\pmb{x}^{t}  \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/8) & 0\\\\\n",
      "0 & -(1/8) \\\\\n",
      "\\end{array} \\bigg] \\pmb{x} + [2.5 \\quad 0 ]\\;\\pmb x - 12.5 - ln(4) \n",
      "= \\pmb{x}^{t} \\cdot( -\\frac{1}{8} \\; \\pmb{x}) + 2.5 x_1 - 12.5 - 2ln(2) $\n",
      "\n",
      "$ \\Rightarrow g_2(\\pmb{x}) = - \\frac{1}{8} (x^{2}_1 + x^{2}_2) +2.5 x_1 - 12.5 - 2ln(2)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{W}_{3} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/10) & 0\\\\\n",
      "0 & -(1/10) \\\\\n",
      "\\end{array} \\bigg] $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\pmb{w}_{3} = \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/5) & 0\\\\\n",
      "0 & (1/5) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "5 \\\\\n",
      "5 \\\\\n",
      "\\end{array} \\bigg] = \\bigg[ \n",
      "\\begin{array}{c}\n",
      "1 \\\\\n",
      "1 \\\\\n",
      "\\end{array} \\bigg]$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "$ \\omega_{30} = -\\frac{1}{2} [5 \\quad 5 ] \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "(1/5) & 0\\\\\n",
      "0 & (1/5) \\\\\n",
      "\\end{array} \\bigg] \\cdot \\bigg[ \n",
      "\\begin{array}{c}\n",
      "5 \\\\\n",
      "5 \\\\\n",
      "\\end{array} \\bigg] \n",
      "- ln(5) = 5 -ln(5)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "with $ \\quad g_3(\\pmb{x}) = \\pmb{x}^{\\,t} \\bigg( - \\frac{1}{2} \\Sigma_3^{-1} \\bigg) \\pmb{x} + \\bigg( \\Sigma_3^{-1} \\pmb{\\mu}_{\\,3}\\bigg)^t \\pmb x + \\bigg( -\\frac{1}{2} \\pmb{\\mu}_{\\,3}^{\\,t} \\Sigma_{3}^{-1} \\pmb{\\mu}_{\\,3} -\\frac{1}{2} ln(|\\Sigma_3|)\\bigg)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\Rightarrow g_3(\\pmb{x}) = \\pmb{x}^{t}  \\bigg[ \n",
      "\\begin{array}{cc}\n",
      "-(1/10) & 0\\\\\n",
      "0 & -(1/10) \\\\\n",
      "\\end{array} \\bigg] \\pmb{x} + [1 \\quad 1 ]\\; \\pmb x + 5 - ln(5) \n",
      "= \\pmb{x}^{t} \\cdot ( - \\frac{1}{10})\\; \\pmb x )+  \\; {x_1} + {x_2} + 5 - ln(5) $\n",
      "\n",
      "$ \\Rightarrow g_3(\\pmb{x}) = - \\frac{1}{10} (x^{2}_1 + x^{2}_2)+  \\; {x_1} + {x_2} + 5 - ln(5)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='decision_boundaries'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='impl_discrfunc'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Implementing the Discriminant Function for arbitrary covariance matrices"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def discriminant_function(x_vec, cov_mat, mu_vec):\n",
      "    \"\"\"\n",
      "    Calculates the value of the discriminant function for a dx1 dimensional\n",
      "    sample given covariance matrix and mean vector.\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        cov_mat: numpy array of the covariance matrix.\n",
      "        mu_vec: dx1 dimensional numpy array of the sample mean.\n",
      "    \n",
      "    Returns a float value as result of the discriminant function.\n",
      "    \n",
      "    \"\"\"\n",
      "    W_i = (-1/2) * np.linalg.inv(cov_mat)\n",
      "    assert(W_i.shape[0] > 1 and W_i.shape[1] > 1),\\\n",
      "        'W_i must be a matrix'\n",
      "    \n",
      "    w_i = np.linalg.inv(cov_mat).dot(mu_vec)\n",
      "    assert(w_i.shape[0] > 1 and w_i.shape[1] == 1),\\\n",
      "        'w_i must be a column vector'\n",
      "    \n",
      "    omega_i_p1 = (((-1/2) * (mu_vec).T).dot(np.linalg.inv(cov_mat))).dot(mu_vec)\n",
      "    omega_i_p2 = (-1/2) * np.log(np.linalg.det(cov_mat))\n",
      "    omega_i = omega_i_p1 - omega_i_p2\n",
      "    assert(omega_i.shape == (1, 1)), 'omega_i must be a scalar'\n",
      "    \n",
      "    g = ((x_vec.T).dot(W_i)).dot(x_vec) + (w_i.T).dot(x_vec) + omega_i\n",
      "    return float(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='decision_rule'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Implementing the decision rule"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import operator\n",
      "\n",
      "def classify_data(x_vec, g, mu_vecs, cov_mats):\n",
      "    \"\"\"\n",
      "    Classifies an input sample into 1 out of 3 classes determined by\n",
      "    maximizing the discriminant function g_i().\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        g: The discriminant function.\n",
      "        mu_vecs: A list of mean vectors as input for g.\n",
      "        cov_mats: A list of covariance matrices as input for g.\n",
      "    \n",
      "    Returns a tuple (g_i()_value, class label).\n",
      "    \n",
      "    \"\"\"\n",
      "    assert(len(mu_vecs) == len(cov_mats)),\\\n",
      "        'Number of mu_vecs and cov_mats must be equal.'\n",
      "    \n",
      "    g_vals = []\n",
      "    for m,c in zip(mu_vecs, cov_mats): \n",
      "        g_vals.append(g(x_vec, mu_vec=m, cov_mat=c))\n",
      "    \n",
      "    max_index, max_value = max(enumerate(g_vals), \n",
      "                               key=operator.itemgetter(1))\n",
      "    return (max_value, max_index + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"empirical_error\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Classifying data and calculating the empirical error"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Known parameters\n",
      "cov_mat_1 = 4 * np.eye(2,2)\n",
      "mu_vec_1 = np.array([[0],[0]])\n",
      "\n",
      "cov_mat_2 = 4 * np.eye(2,2)\n",
      "mu_vec_2 = np.array([[10],[0]])\n",
      "\n",
      "cov_mat_3 = 5 * np.eye(2,2)\n",
      "mu_vec_3 = np.array([[5],[5]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='emp_err_train'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Empirical error of the training dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def empirical_error(data_set, classes, classifier_func, classifier_func_args):\n",
      "    \"\"\"\n",
      "    Keyword arguments:\n",
      "        data_set: 'n x d'- dimensional numpy array, \n",
      "            class label in the last column.\n",
      "        classes: List of the class labels.\n",
      "        classifier_func: Function that returns the \n",
      "            max argument from the discriminant function.\n",
      "            evaluation and the class label as a tuple.\n",
      "        classifier_func_args: List of arguments for the 'classifier_func'.\n",
      "    \n",
      "    Returns a tuple, consisting of a dictionary \n",
      "        with the classif. counts and the error.\n",
      "    \n",
      "    e.g., ( {1: {1: 321, 2: 5}, 2: {1: 0, 2: 317}}, 0.05)\n",
      "    where keys are class labels, and values are \n",
      "    sub-dicts counting for which class (key)\n",
      "    how many samples where classified as such.\n",
      "    \n",
      "    \"\"\"\n",
      "    class_dict = {i:{j:0 for j in classes} for i in classes}\n",
      "\n",
      "    for cl in classes:\n",
      "        for row in data_set[data_set[:,-1] == cl][:,:-1]:\n",
      "            g = classifier_func(row, *classifier_func_args)\n",
      "            class_dict[cl][g[1]] += 1\n",
      "    \n",
      "    correct = 0\n",
      "    for i in classes:\n",
      "        correct += class_dict[i][i]\n",
      "    \n",
      "    misclass = data_set.shape[0] - correct\n",
      "    return (class_dict, misclass / data_set.shape[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "classification_dict, error = empirical_error(train_set, [1,2,3], \n",
      "        classify_data, [discriminant_function,\n",
      "        [mu_vec_1, mu_vec_2, mu_vec_3],\n",
      "        [cov_mat_1, cov_mat_2, cov_mat_3]])\n",
      "\n",
      "labels_predicted = ['w{} (predicted)'.format(i) for i in [1,2,3]]\n",
      "labels_predicted.insert(0,'training dataset')\n",
      "\n",
      "train_conf_mat = prettytable.PrettyTable(labels_predicted)\n",
      "for i in [1,2,3]:\n",
      "    a, b, c = [classification_dict[i][j] for j in [1,2,3]]\n",
      "    # workaround to unpack (since Python does not support just '*a')\n",
      "    train_conf_mat.add_row(['w{} (actual)'.format(i), a, b, c])\n",
      "print(train_conf_mat)\n",
      "print('Empirical Error: {:.2f} ({:.2f}%)'.format(error, error * 100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+------------------+----------------+----------------+----------------+\n",
        "| training dataset | w1 (predicted) | w2 (predicted) | w3 (predicted) |\n",
        "+------------------+----------------+----------------+----------------+\n",
        "|   w1 (actual)    |      321       |       5        |       24       |\n",
        "|   w2 (actual)    |       0        |      317       |       33       |\n",
        "|   w3 (actual)    |       8        |       13       |      329       |\n",
        "+------------------+----------------+----------------+----------------+\n",
        "Empirical Error: 0.08 (7.90%)\n"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='emp_err_test'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Empirical error of the test dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "classification_dict, error = empirical_error(test_set, [1,2,3], \n",
      "        classify_data, [discriminant_function,\n",
      "        [mu_vec_1, mu_vec_2, mu_vec_3],\n",
      "        [cov_mat_1, cov_mat_2, cov_mat_3]])\n",
      "\n",
      "labels_predicted = ['w{} (predicted)'.format(i) for i in [1,2,3]]\n",
      "labels_predicted.insert(0,'test dataset')\n",
      "\n",
      "train_conf_mat = prettytable.PrettyTable(labels_predicted)\n",
      "for i in [1,2,3]:\n",
      "    a, b, c = [classification_dict[i][j] for j in [1,2,3]]\n",
      "    # workaround to unpack (since Python does not support just '*a')\n",
      "    train_conf_mat.add_row(['w{} (actual)'.format(i), a, b, c])\n",
      "print(train_conf_mat)\n",
      "print('Empirical Error: {:.2f} ({:.2f}%)'.format(error, error * 100))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+--------------+----------------+----------------+----------------+\n",
        "| test dataset | w1 (predicted) | w2 (predicted) | w3 (predicted) |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "| w1 (actual)  |      142       |       0        |       8        |\n",
        "| w2 (actual)  |       1        |      142       |       7        |\n",
        "| w3 (actual)  |       1        |       3        |      146       |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "Empirical Error: 0.04 (4.44%)\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<a name='task_1b'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1b)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "<a name=\"descr_task_1b\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "Suppose $p([x_1,x_2]^t\\;|\\;\\omega_i) \\;\u223c \\; N(\\pmb \\mu_i,\\pmb \\Sigma_i), \\; i = 1,2,3$, where the $\\pmb \\mu_i$\u2019s and $\\pmb \\Sigma_i$\u2019s are unknown.  \n",
      "Use the training set to compute the MLE of the \u03bci\u2019s and the $\\pmb \\Sigma_i$\u2019s.  \n",
      "What is the error rate on the test set when the Bayes decision rule using  \n",
      "the estimated parameters is employed for classification?\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='about_mle'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## About the Maximum Likelihood Estimate (MLE)\n",
      "\n",
      "In contrast to task one, now we only know the number of parameters for the class conditional densities  \n",
      "$p (\\; \\pmb x \\; | \\; \\omega_i)$ and we want to use a Maximum Likelihood Estimatation (MLE)  \n",
      "to estimate the quantities of these parameters from the training data.\n",
      "\n",
      "\n",
      "Given the information about the form of the model - the data is normal distributed -  \n",
      "the 2 parameters to be estimated are $\\pmb \\mu_i$ and $\\pmb \\Sigma_i$,  \n",
      "which are summarized by the parameter vector  \n",
      "$\\pmb \\theta_i = \\bigg[ \\begin{array}{c}\n",
      "\\ \\theta_{i1} \\\\\n",
      "\\ \\theta_{i2} \\\\\n",
      "\\end{array} \\bigg]=\n",
      "\\bigg[ \\begin{array}{c}\n",
      "\\pmb \\mu_i \\\\\n",
      "\\pmb \\Sigma_i \\\\\n",
      "\\end{array} \\bigg]$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For the Maximum Likelihood Estimate (MLE), we assume that we have a set of samles  \n",
      "$D = \\left\\{ \\pmb x_1, \\pmb x_2,..., \\pmb x_n \\right\\} $ that are *i.i.d.*  \n",
      "(independent and identically distributed, drawn with probability  \n",
      "$p(\\pmb x \\; | \\; \\omega_i, \\; \\pmb \\theta_i) $).  \n",
      "Thus, we can **work with each class separately** and omit the class labels,  \n",
      "so that we write the probability density as $p(\\pmb x \\; | \\; \\pmb \\theta)$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "##### Likelihood of $ \\pmb \\theta $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thus, the probability of observing $D = \\left\\{ \\pmb x_1, \\pmb x_2,..., \\pmb x_n \\right\\} $ is: \n",
      "<br>\n",
      "<br>\n",
      "$p(D\\; | \\;  \\pmb \\theta\\;) = p(\\pmb x_1 \\; | \\; \\pmb \\theta\\;)\\; \\cdot \\; p(\\pmb x_2 \\; | \\;\\pmb \\theta\\;) \\; \\cdot \\;...  \\; p(\\pmb x_n \\; | \\; \\pmb \\theta\\;) = \\prod_{k=1}^{n} \\; p(\\pmb x_k \\pmb \\; | \\; \\pmb \\theta \\;)$  \n",
      "<br>\n",
      "Where $p(D\\; | \\;  \\pmb  \\theta\\;)$ is also called the ***likelihood of $\\pmb\\ \\theta$***."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are given the information that $p([x_1,x_2]^t) \\;\u223c \\; N(\\pmb \\mu,\\pmb \\Sigma) $  \n",
      "(remember that we dropped the class labels,  \n",
      "since we are working with every class separately)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the mutlivariate normal density is given as:  \n",
      "$\\quad \\quad p(\\pmb x) = \\frac{1}{(2\\pi)^{d/2} \\; |\\Sigma|^{1/2}} exp \\bigg[ -\\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\Sigma^{-1}(\\pmb x - \\pmb \\mu) \\bigg]$\n",
      "\n",
      "So that  \n",
      "$p(D\\; | \\;  \\pmb \\theta\\;) = \\prod_{k=1}^{n} \\; p(\\pmb x_k \\pmb \\; | \\; \\pmb \\theta \\;) =  \\prod_{k=1}^{n} \\; \\frac{1}{(2\\pi)^{d/2} \\; |\\Sigma|^{1/2}} exp \\bigg[ -\\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\Sigma^{-1}(\\pmb x - \\pmb \\mu) \\bigg]$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since it is easier to work with the logarithm, we define the log-likelihood function $l(\\pmb \\theta)$ as  \n",
      "$l(\\pmb \\theta) \\equiv ln\\; p(D\\; | \\;  \\pmb \\theta\\;) = \\sum\\limits_{k=1}^{n} \\;ln\\; p(\\pmb x_k \\pmb \\; | \\; \\pmb \\theta \\;)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and the log of the multivariate density"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ l(\\pmb\\theta) =  \\sum\\limits_{k=1}^{n} - \\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\pmb \\Sigma^{-1} \\; (\\pmb x - \\pmb \\mu) - \\frac{d}{2} \\; ln \\; 2\\pi - \\frac{1}{2} \\;ln \\; |\\pmb\\Sigma|$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "#### Maximum Likelihood Estimate (MLE)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to obtain the MLE $\\boldsymbol{\\hat{\\theta}}$, we maximize $l (\\pmb  \\theta)$, which can be done via differentiation:\n",
      "\n",
      "with \n",
      "$\\nabla_{\\pmb \\theta} \\equiv \\begin{bmatrix}  \n",
      "\\frac{\\partial \\; }{\\partial \\; \\theta_1} \\\\ \n",
      "\\frac{\\partial \\; }{\\partial \\; \\theta_2}\n",
      "\\end{bmatrix} = \\begin{bmatrix} \n",
      "\\frac{\\partial \\; }{\\partial \\; \\pmb \\mu} \\\\ \n",
      "\\frac{\\partial \\; }{\\partial \\; \\pmb \\sigma}\n",
      "\\end{bmatrix}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$\\nabla_{\\pmb \\theta} l = \\sum\\limits_{k=1}^n \\nabla_{\\pmb \\theta} \\;ln\\; p(\\pmb x| \\pmb \\theta) = 0 $"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='mle_mu'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## MLE of the mean vector $\\pmb \\mu$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The MLE of the parameter $\\pmb\\mu$ is given by the equation:  \n",
      "${\\hat{\\pmb\\mu}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} \\pmb x_k$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "mu_est_1 = np.sum(train_set[train_set[:,2] == 1], \n",
      "                  axis=0)/len(train_set[train_set[:,2] == 1])\n",
      "mu_est_1 = mu_est_1[0:2].reshape(2,1)\n",
      "mu_est_2 = np.sum(train_set[train_set[:,2] == 2], \n",
      "                  axis=0)/len(train_set[train_set[:,2] == 2])\n",
      "mu_est_2 = mu_est_2[0:2].reshape(2,1)\n",
      "mu_est_3 = np.sum(train_set[train_set[:,2] == 3], \n",
      "                  axis=0)/len(train_set[train_set[:,2] == 3])\n",
      "mu_est_3 = mu_est_3[0:2].reshape(2,1)\n",
      "\n",
      "print(mu_est_1)\n",
      "mu_mle = prettytable.PrettyTable([\"\", \"mu_1\", \"mu_2\", \"mu_3\"])\n",
      "mu_mle.add_row([\"MLE\",mu_est_1, mu_est_2, mu_est_3])\n",
      "mu_mle.add_row([\"actual\",mu_vec_1, mu_vec_2, mu_vec_3])\n",
      "\n",
      "print(mu_mle)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.19298829]\n",
        " [-0.04618914]]\n",
        "+--------+-----------------+-----------------+-----------------+\n",
        "|        |       mu_1      |       mu_2      |       mu_3      |\n",
        "+--------+-----------------+-----------------+-----------------+\n",
        "|  MLE   |  [[ 0.19298829] |  [[ 9.916212  ] |  [[ 5.06426086] |\n",
        "|        |  [-0.04618914]] |  [ 0.05908629]] |  [ 4.88450171]] |\n",
        "| actual |       [[0]      |      [[10]      |       [[5]      |\n",
        "|        |       [0]]      |       [ 0]]     |       [5]]      |\n",
        "+--------+-----------------+-----------------+-----------------+\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='mle_sigma'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## MLE of the covariance matrix $\\pmb \\Sigma$\n",
      "\n",
      "The MLE of the parameter $\\pmb\\Sigma$ is given by the equation:  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "${\\hat{\\pmb\\Sigma}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} (\\pmb x_k - \\hat{\\mu})(\\pmb x_k - \\hat{\\mu})^t$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mle_est_cov(x_samples, mu_est):\n",
      "    \"\"\"\n",
      "    Calculates the Maximum Likelihood Estimate for the covariance matrix.\n",
      "    \n",
      "    Keyword Arguments:\n",
      "        x_samples: np.array of the samples for 1 class, n x d dimensional \n",
      "        mu_est: np.array of the mean MLE, d x 1 dimensional\n",
      "        \n",
      "    Returns the MLE for the covariance matrix as d x d numpy array.\n",
      "    \n",
      "    \"\"\"\n",
      "    cov_est = np.zeros((2,2))\n",
      "    for x_vec in x_samples:\n",
      "        x_vec = x_vec.reshape(2,1)\n",
      "        assert(x_vec.shape == mu_est.shape),\\\n",
      "        'mean and x vector hmust be of equal shape'\n",
      "        cov_est += (x_vec - mu_est).dot((x_vec - mu_est).T)\n",
      "    return cov_est / len(x_samples)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "class1_train_samples1 = train_set[train_set[:,2] == 1][:,0:2]\n",
      "cov_est_1 = mle_est_cov(class1_train_samples1, mu_est_1)\n",
      "\n",
      "class1_train_samples2 = train_set[train_set[:,2] == 2][:,0:2]\n",
      "cov_est_2 = mle_est_cov(class1_train_samples2, mu_est_2)\n",
      "\n",
      "class1_train_samples3 = train_set[train_set[:,2] == 3][:,0:2]\n",
      "cov_est_3 = mle_est_cov(class1_train_samples3, mu_est_3)\n",
      "\n",
      "cov_mle = prettytable.PrettyTable([\"\", \"covariance_matrix_1\",\\\n",
      "                                   \"covariance_matrix_2\", \"covariance_matrix_3\"])\n",
      "cov_mle.add_row([\"MLE\", cov_est_1, cov_est_2, cov_est_3])\n",
      "cov_mle.add_row(['','','',''])\n",
      "cov_mle.add_row([\"actual\", cov_mat_1, cov_mat_2, cov_mat_3])\n",
      "\n",
      "print(cov_mle)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+--------+---------------------------------------+-----------------------------+-----------------------------+\n",
        "|        |          covariance_matrix_1          |     covariance_matrix_2     |     covariance_matrix_3     |\n",
        "+--------+---------------------------------------+-----------------------------+-----------------------------+\n",
        "|  MLE   |  [[  4.99895563e+00   2.48237701e-03] |  [[ 5.4868865  -0.02521011] |  [[ 3.95020679 -0.02051392] |\n",
        "|        |  [  2.48237701e-03   4.73000833e+00]] |  [-0.02521011  5.05639885]] |  [-0.02051392  4.51383839]] |\n",
        "|        |                                       |                             |                             |\n",
        "| actual |               [[ 4.  0.]              |          [[ 4.  0.]         |          [[ 5.  0.]         |\n",
        "|        |               [ 0.  4.]]              |          [ 0.  4.]]         |          [ 0.  5.]]         |\n",
        "+--------+---------------------------------------+-----------------------------+-----------------------------+\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='error_mle'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Error on the test set using the estimated parameters\n",
      "\n",
      "Using the estimated parameters $\\pmb \\mu_i$ and $\\pmb \\Sigma_i$, which we obtained  \n",
      "via MLE, we calculate the error on the test data set again. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "classification_dict, error = empirical_error(test_set, \n",
      "        [1,2,3], classify_data, [discriminant_function,\n",
      "        [mu_est_1, mu_est_2, mu_est_3],\n",
      "        [cov_est_1, cov_est_2, cov_est_3]])\n",
      "\n",
      "labels_predicted = ['w{} (predicted)'.format(i) for i in [1,2,3]]\n",
      "labels_predicted.insert(0,'test dataset')\n",
      "\n",
      "train_conf_mat = prettytable.PrettyTable(labels_predicted)\n",
      "for i in [1,2,3]:\n",
      "    a, b, c = [classification_dict[i][j] for j in [1,2,3]]\n",
      "    # workaround to unpack (since Python does not support just '*a')\n",
      "    train_conf_mat.add_row(['w{} (actual)'.format(i), a, b, c])\n",
      "print(train_conf_mat)\n",
      "print('Empirical Error: {:.2f} ({:.2f}%)'.format(error, error * 100))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+--------------+----------------+----------------+----------------+\n",
        "| test dataset | w1 (predicted) | w2 (predicted) | w3 (predicted) |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "| w1 (actual)  |      143       |       0        |       7        |\n",
        "| w2 (actual)  |       1        |      147       |       2        |\n",
        "| w3 (actual)  |       6        |       5        |      139       |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "Empirical Error: 0.05 (4.67%)\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='task_1c'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1 c)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='description_1c'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Description"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "\n",
      "Suppose the form of the distributions of $p([x_1, x_2]^t\\;|\\;\\omega_i),\\; i = 1,2,3$ is unknown.  \n",
      "Assume that the training dataset can be used to estimate the density at a  \n",
      "point using the Parzen window technique (a spherical Gaussian kernel with h = 1).  \n",
      "What is the error rate on the test set when the Bayes decision rule is employed for classification?\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The density for a point *x* is defined as  \n",
      "\n",
      "$p(\\pmb x) = \\frac{k / n}{V}$\n",
      "\n",
      "where *k* is the number of points inside a specified region, *n* is the  \n",
      "number of total points, and *V* is the volume of that region.\n",
      "\n",
      "Starting with a simple example to count the number $k_n$, the  \n",
      "equation using a hypercube would be  \n",
      "$k_n = \\sum\\limits_{i=1}^{n} \\phi \\bigg( \\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg)$ \n",
      "\n",
      "where $\\phi(\\pmb u)$ is the window function, and here: $\\pmb u = \\bigg( \\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg)$ "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on this window, we can now formulate the Parzen-window  \n",
      "estimation with a hypercube kernel as follows:\n",
      "\n",
      "$P_n(\\pmb x) = \\frac{1}{n} \\sum\\limits_{i=1}^{n} \\frac{1}{h^d} \\phi \\bigg[ \\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg]$  \n",
      "\n",
      "where   \n",
      "$h^d = V_n\\quad$   and    $\\quad\\phi \\bigg[ \\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg] = k$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For the Gaussian kernel (instead of the hypercube), we can just simply swap the terms of the window function by:  \n",
      "\n",
      "$\\frac{1}{(\\sqrt {2 \\pi})^d h_{n}^{d}} exp \\; \\bigg[ -\\frac{1}{2} \\bigg(\\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg)^2 \\bigg]$\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So that we have the Parzen window estimation becomes:  \n",
      "\n",
      "$P_n(\\pmb x) = \\frac{1}{n} \\sum\\limits_{i=1}^{n} \\frac{1}{h^d} \\phi \\Bigg[ \\frac{1}{(\\sqrt {2 \\pi})^d h_{n}^{d}} exp \\; \\bigg[ -\\frac{1}{2} \\bigg(\\frac{\\pmb x - \\pmb x_i}{h_n} \\bigg)^2 \\bigg] \\Bigg]$  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name=\"use_parzen\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Using the Parzen-window technique for Gaussian kernel desnity estimation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import kde\n",
      "class1_kde = kde.gaussian_kde(\n",
      "    train_set[train_set[:,2] == 1].T[0:2], bw_method=1.0)\n",
      "class2_kde = kde.gaussian_kde(\n",
      "    train_set[train_set[:,2] == 2].T[0:2], bw_method=1.0)\n",
      "class3_kde = kde.gaussian_kde(\n",
      "    train_set[train_set[:,2] == 3].T[0:2], bw_method=1.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "from matplotlib.mlab import bivariate_normal\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "\n",
      "X = np.linspace(-10, 20, 100)\n",
      "Y = np.linspace(-10, 20, 100)\n",
      "X,Y = np.meshgrid(X,Y)\n",
      "\n",
      "for bwmethod,title in zip([class1_kde, class2_kde, class3_kde], \n",
      "                          ['class1', 'class2', 'class3']):\n",
      "    fig = plt.figure(figsize=(10, 7))\n",
      "    ax = fig.gca(projection='3d')\n",
      "    Z = bwmethod(np.array([X.ravel(),Y.ravel()]))\n",
      "    Z = Z.reshape(100,100)\n",
      "    surf = ax.plot_surface(X, Y, Z, rstride=1, \n",
      "        cstride=1, cmap=plt.cm.coolwarm,\n",
      "        linewidth=0, antialiased=False)\n",
      "\n",
      "    ax.set_zlim(0, 0.02)\n",
      "    ax.zaxis.set_major_locator(plt.LinearLocator(10))\n",
      "    ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))\n",
      "    ax.set_xlabel('X')\n",
      "    ax.set_ylabel('Y')\n",
      "    ax.set_zlabel('p(x)')\n",
      "    \n",
      "    plt.title('Gaussian kernel, bw_method %s' %title)\n",
      "    fig.colorbar(surf, shrink=0.5, aspect=7, cmap=plt.cm.coolwarm)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAGUCAYAAAAF0TmYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCO7/fdK5NOs8evYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105960b70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAGUCAYAAAAF0TmYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw2UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107710be0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAGUCAYAAAAF0TmYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niRBCCCE0J4FECCGEEJr7PxNIb4qE5qgVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10719da58>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='bayes_rule_parzen'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "###Implementing the classifier using  Bayes' decision rule\n",
      "\n",
      "\n",
      "$decide \\; \\omega_j \\; for \\;\\; max \\;\\bigg[ P(\\omega_j|x) = \\frac{p(x|\\omega_j) * P(\\omega_j)}{p(x)} \\bigg]\\;, \\;\\; j = 1, 2, 3 $\n",
      "\n",
      "We can remove the prior probabilities (equal priors) and the scale factor:\n",
      "\n",
      "\n",
      "$decide \\; \\omega_j \\; for \\;\\; max \\;[ p(x|\\omega_j) ]\\;, \\;\\; j = 1, 2, 3 $"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import operator\n",
      "\n",
      "def bayes_classifier(x_vec, kdes):\n",
      "    \"\"\"\n",
      "    Classifies an input sample into class w_j determined by\n",
      "    maximizing the class conditional probability for p(x|w_j).\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        kdes: List of the gausssian_kde (kernel density) estimates\n",
      "      \n",
      "    Returns a tuple ( p(x|w_j)_value, class label ).\n",
      "    \n",
      "    \"\"\"\n",
      "    p_vals = []\n",
      "    for kde in kdes:\n",
      "        p_vals.append(kde.evaluate(x_vec))\n",
      "    max_index, max_value = max(enumerate(p_vals), key=operator.itemgetter(1))\n",
      "    return (max_value, max_index + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='error_parzen'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Error on the test set using the likelihoods \n",
      "## from the Gaussian kernel estimate of the Parzen-window technique"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "classification_dict, error = empirical_error(test_set, [1,2,3],  \n",
      "                bayes_classifier, [[class1_kde, class2_kde, class3_kde]])\n",
      "\n",
      "labels_predicted = ['w{} (predicted)'.format(i) for i in [1,2,3]]\n",
      "labels_predicted.insert(0,'test dataset')\n",
      "\n",
      "train_conf_mat = prettytable.PrettyTable(labels_predicted)\n",
      "for i in [1,2,3]:\n",
      "    a, b, c = [classification_dict[i][j] for j in [1,2,3]]\n",
      "    # workaround to unpack (since Python does not support just '*a')\n",
      "    train_conf_mat.add_row(['w{} (actual)'.format(i), a, b, c])\n",
      "print(train_conf_mat)\n",
      "print('Empirical Error: {:.2f} ({:.2f}%)'.format(error, error * 100))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+--------------+----------------+----------------+----------------+\n",
        "| test dataset | w1 (predicted) | w2 (predicted) | w3 (predicted) |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "| w1 (actual)  |      142       |       0        |       8        |\n",
        "| w2 (actual)  |       1        |      144       |       5        |\n",
        "| w3 (actual)  |       3        |       3        |      144       |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "Empirical Error: 0.04 (4.44%)\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='task_1d'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "# 1d)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='description_1d'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Description\n",
      "Suppose the 1-nearest neighbor method is used for classifying the patterns in the test set.  \n",
      "What is the error rate of this approach?\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"about_knn\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### About the k-Nearest Neighbor Technique\n",
      "\n",
      "As mentioned previously for the Parzen-window approach,  \n",
      "The density for a point *x* is defined as  \n",
      "\n",
      "$p(\\pmb x) = \\frac{k / n}{V}$\n",
      "\n",
      "where *k* is the number of points inside a specified region, *n* is the  \n",
      "number of total points, and *V* is the volume of that region.\n",
      "\n",
      "However, in contrast to the Parzen-window technique, we don't have a  \n",
      "fixed *volume* parameter, but control *k* (the number of points in a region  \n",
      "of specfic volume). In other words, we set the number of points that we want to  \n",
      "capture and compute the volume of the region. Also, the same convergence assumptions  \n",
      "like for the Parzen-window technique apply for the k-Nearest Neighbor technque.   \n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Special case: 1-Nearest Neighbor\n",
      "Here, we are using a simpler variant of the *knn*-algorithm,  \n",
      "where we classify a point as belonging to the class of its nearest neighbor."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name=\"implement_knn\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Implementing the code for finding the 1-Nearest Neighbor"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_1nearest_neighbor(p, dataset):\n",
      "    \"\"\"\n",
      "    Finds the nearest neighbor of a point in a dataset.\n",
      "    \n",
      "    Keyword arguments:\n",
      "        p: Query point as dx1-dimensional numpy array.\n",
      "        dataset: Dataset as nxd-dimensional numpy array \n",
      "            with class label in the last column.\n",
      "            \n",
      "    Returns the nearest neighbor as 1xd dimensional numpy\n",
      "        array from the dataset.\n",
      "\n",
      "\"\"\"\n",
      "    p_index = np.argmin([np.inner(p-x, p-x) for x in dataset[:,0:-1]])\n",
      "    return dataset[p_index]   \n",
      "\n",
      "p = np.array([[1],[1]])\n",
      "find_1nearest_neighbor(p, test_set)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def one_nn_classify(train_set, data_set):\n",
      "    \"\"\"\n",
      "    Classifies points in a dataset based on the\n",
      "    nearest neighbor in the training dataset\n",
      "    (1-nearest neighbor technique).\n",
      "    \n",
      "    Keyword arguments:\n",
      "        train_set: training points as nx3-dimensional numpy array.\n",
      "            with class label in the last column\n",
      "        data_set: training points as nx2-dimensional numpy array.\n",
      "            \n",
      "    Returns a list of the predicted class labels.\n",
      "\n",
      "\"\"\"\n",
      "    class_labels = [np.argmin([np.inner(p-x, p-x)  \n",
      "                               for x in train_set[:,0:-1]])for p in data_set]\n",
      "    return [int(train_set[cl,-1]) for cl in class_labels]\n",
      "\n",
      "true_class = test_set[:,-1]\n",
      "predicted_class = one_nn_classify(train_set, test_set[:,0:-1])   \n",
      "error = sum([1 for pred,true in zip(predicted_class,true_class)  \n",
      "             if pred!=true])/len(test_set)\n",
      "print(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0866666666667\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "c1_pred = [0, 0, 0]\n",
      "c2_pred = [0, 0, 0]\n",
      "c3_pred = [0, 0, 0]\n",
      "\n",
      "for pred,true in zip(predicted_class,true_class):\n",
      "    if true == 1:\n",
      "        c1_pred[pred-1] += 1\n",
      "    elif true == 2:\n",
      "        c2_pred[pred-1] += 2\n",
      "    else:\n",
      "        c3_pred[pred-1] += 3        \n",
      "\n",
      "\n",
      "labels_predicted = ['w{} (predicted)'.format(i) for i in [1,2,3]]\n",
      "labels_predicted.insert(0,'test dataset')\n",
      "\n",
      "train_conf_mat = prettytable.PrettyTable(labels_predicted)\n",
      "for i in [1,2,3]:\n",
      "    a, b, c = c1_pred[i-1], c2_pred[i-1], c3_pred[i-1]\n",
      "    # workaround to unpack (since Python does not support just '*a')\n",
      "    train_conf_mat.add_row(['w{} (actual)'.format(i), a, b, c])\n",
      "print(train_conf_mat)\n",
      "print('Empirical Error: {:.2f} ({:.2f}%)'.format(error, error * 100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+--------------+----------------+----------------+----------------+\n",
        "| test dataset | w1 (predicted) | w2 (predicted) | w3 (predicted) |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "| w1 (actual)  |      139       |       6        |       24       |\n",
        "| w2 (actual)  |       0        |      276       |       24       |\n",
        "| w3 (actual)  |       11       |       18       |      402       |\n",
        "+--------------+----------------+----------------+----------------+\n",
        "Empirical Error: 0.09 (8.67%)\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='task_1e'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1e)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='description_1e'></a>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Description\n",
      "Repeat the above three classification procedures by varying the size of the  \n",
      "training set as follows: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% of each class.  \n",
      "Plot the error rate as a function of the size of the training set for each of the 3 cases."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='shrink_train'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Creating training sets with variable size"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This workaround is necessary to make sure that every subset \n",
      "# contains same number of\n",
      "# samples for each of the three classes\n",
      "\n",
      "train_subsets_c1 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 1]\n",
      "    train_subsets_c1[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "    \n",
      "train_subsets_c2 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 2]\n",
      "    train_subsets_c2[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "    \n",
      "train_subsets_c3 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 3]\n",
      "    train_subsets_c3[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "\n",
      "# combine separate-class subsets for convenience\n",
      "train_subsets = {}\n",
      "for set_size in sorted(train_subsets_c1.keys()):\n",
      "    t_set = np.zeros(shape=(1,3))\n",
      "    for subset in [train_subsets_c1, train_subsets_c2, train_subsets_c3]:        \n",
      "        t_set = np.append(t_set, subset[set_size], axis=0)\n",
      "        # delete the first placeholder row used for initializing the array                                  \n",
      "    t_set = np.delete(t_set, 0, axis=0)\n",
      "    train_subsets[set_size] = t_set\n",
      "\n",
      "for i in sorted(train_subsets):\n",
      "    print(i, train_subsets[i].shape)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 (105, 3)\n",
        "20 (210, 3)\n",
        "30 (315, 3)\n",
        "40 (420, 3)\n",
        "50 (525, 3)\n",
        "60 (630, 3)\n",
        "70 (732, 3)\n",
        "80 (840, 3)\n",
        "90 (945, 3)\n",
        "100 (1050, 3)\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plotting one subset to confirm that the reduction worked error-free\n",
      "\n",
      "f, ax = plt.subplots(figsize=(7, 7))\n",
      "ax.scatter(train_subsets[30][train_subsets[30][:,2] == 1][:,0], \n",
      "           train_subsets[30][train_subsets[30][:,2] == 1][:,1], \\\n",
      "           marker='o', color='green', s=40, alpha=0.5, label='$\\omega_1$')\n",
      "ax.scatter(train_subsets[30][train_subsets[30][:,2] == 2][:,0], \n",
      "           train_subsets[30][train_subsets[30][:,2] == 2][:,1], \\\n",
      "           marker='^', color='red', s=40, alpha=0.5, label='$\\omega_2$')\n",
      "ax.scatter(train_subsets[30][train_subsets[30][:,2] == 3][:,0], \n",
      "           train_subsets[30][train_subsets[30][:,2] == 3][:,1], \\\n",
      "           marker='s', color='blue', s=40, alpha=0.5, label='$\\omega_3$')\n",
      "plt.legend(loc='upper right') \n",
      "plt.title('Training datset after reducing the size to 30%', size=20)\n",
      "plt.ylabel('$x_2$', size=20)\n",
      "plt.xlabel('$x_1$', size=20)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAHPCAYAAACGMHkxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcE2f+B/DPJBwhhCPcp4ICgorifVesV6urFrV361l3\n2+2xtcfW/qor9vJo163t9m636nZbW4+i1tar9ULRAhW88AABuQW55Uzy/P6YTiAkgSQEkuD3/Xrx\nEmeSmWfCZL7zfeY5OMYYAyGEEEKsgsjSBSCEEEJICwrMhBBCiBWhwEwIIYRYEQrMhBBCiBWhwEwI\nIYRYEQrMhBBCiBXpkYFZJBJh0qRJnd5ObGwsRCLb+YhycnIgEomwePFiSxfFZnz77bcYMmQIXFxc\nIBKJsHz5cksXyaIscc7Hx8dDJBLh2LFj3brfzjh69ChEIhHWrFlj6aK0a/PmzRCJRNiyZYuli0KM\n0CXfQJFIZNRPV5w0HMeZZRvm2E5362yZLXnRWbRoEUQiEW7cuNHl+0pKSsKjjz6K27dv4+mnn0Z8\nfDzuvfdem7nodpXuPueF75k1fdcMvcm1pjLrYk2fbXd+t1955RVMnjwZwcHBkEql8PDwwODBg7Fy\n5UqUlJTofd+pU6cwY8YMeHh4QCqVYvDgwdi0aRNUKpXWa4uLi/HII4/Ax8cHfn5+ePzxx1FaWqpz\nuytXroRcLkdRUZFB5bcz7DCNs3r1ao0TgTGG9957D1VVVXj++efh7u6u8fohQ4aYdf+XL1+GVCrt\n9Ha2bt2K+vp6M5TINlnqy9xd+923bx8A/u88evRo9fKjR492aznudM888wwefvhhBAcHW7ooasLf\n3tbPgbi4OIwZMwZ+fn6WLgqA7vs833vvPQwbNgzTp0+Hj48Pbt++jaSkJLz99tv47LPPcPLkSYSH\nh2u8Z/fu3Zg3bx6kUikefPBBeHh4YM+ePVi+fDlOnjyJ77//Xv1alUqFWbNm4dKlS1i8eDFu376N\nr7/+GpmZmTh16pTGcaalpWHDhg345JNP4O/vb1D5uywwt/XVV1+huroazz//PHr16tUVu1WLiIgw\ny3as6UJhCZYaFI4x1i37LiwsBAC9XxYaFK97eHp6wtPT09LF0CD87W39HHB1dYWrq6uli6HWXd/t\nmpoaODg4aC1fuXIl3n77baxbtw5ffvmlenl1dTWWLVsGe3t7HD16FEOHDgUAvP7667j77ruxY8cO\nfPfdd3jwwQcBAMnJyUhNTcXWrVvx2GOPAQBCQ0MRHx+PlJQUjBgxAgCgUCiwePFiTJo0CUuWLDH8\nAFg36d27NxOJRCw3N1dj+cSJExnHcaypqYmtWbOGRUREMEdHR7Zo0SLGGGNVVVVsw4YNbNKkSSww\nMJA5ODgwb29vNnv2bJaUlKRzXxzHsdjYWI1lq1evZhzHsaNHj7Lt27ezESNGMKlUyjw8PNhDDz3E\nCgoKtLYjlK21I0eOMI7jWHx8PDt79iybMWMGc3NzY1KplE2cOJGdOnVKZ5kKCwvZokWLmLe3N3Ny\ncmIxMTFsy5YtGtszVHV1NVu+fDkLDAxkEomERUZGso0bN7KsrCzGcRxbvHixxuuvXLnCXnnlFTZs\n2DDm5eXFHB0dWe/evdmf//xnlp+fr/HahQsXMo7jdP4cO3aMMcZYY2Mj27RpExsyZAiTy+VMKpWy\nkJAQNmfOHHb48GGt8mZkZLCFCxeyoKAg5uDgwHx9fdkjjzzCrly5ovE6ffsNCQnp8DMx5jz56quv\n9O5r0aJFHR6/4JtvvmGxsbHMzc2NSSQSFhUVxd58803W2NioVT7hnCwuLmZLly5lAQEBTCwWs82b\nN7d7XK3PjzNnzrAZM2YwuVzOOI7T+C4ZUxbGGPv222/Z0KFDmZOTE/Px8WGPP/44KygoYBMnTmQi\nkUjn56WvrLq+b4wxplAo2Mcff8zGjh3LXF1dmZOTEwsLC2NPPPEEu3btmvp1wnez7ecrbLesrIwt\nW7aM+fn5MUdHRzZgwAD21Vdf6SxLQ0MDW716NQsNDWWOjo4sNDSUrVy5kjU0NOgtZ1tCeXT9CJ+B\n8HdZs2aNUdeB5uZm9uGHH7JRo0YxFxcXJpVK2ZAhQ9i///1vplKpOiybICsriy1btoz17duXOTk5\nMQ8PDxYdHc2efPJJduvWLfXrdP3t2vuO6/u+GXt+tWXovq5evcoef/xxFhAQwBwcHFhAQABbsGCB\nxvnSGWlpaYzjOHbPPfdoLP/yyy/V3/+2fv31V8ZxHJs4caJ62Xfffcc4jtO4hv3000+M4zi2Y8cO\n9bI33niDubq6asW9jnRJxmyKuXPnIiUlBTNmzMDcuXPh4+MDALh06RJWrlyJiRMnYtasWZDL5cjN\nzcWePXvw888/Y+/evZg+fbrW9vRVmXz00UfYs2cP5syZg0mTJuH06dP47rvvkJ6ejrS0NK27LH3b\nSUlJwYYNGzB27Fj8+c9/Rm5uLnbu3InJkycjLS1NI2u/efMmxowZgxs3bmDixIkYO3YsioqK8Ne/\n/hVTp05tdz9tNTY2YvLkyUhJSUFMTAwef/xxVFRU4I033lBXwba1a9cufPrpp7j77rsxfvx4ODg4\n4MKFC/jiiy+wd+9epKSkICAgAABf9cVxHLZs2YLY2FjExsaqtxMSEgKAf1a0bds2REdHY+HChXBy\nckJBQQFOnjyJAwcOYPLkyer37N+/H3PnzoVSqcSsWbMQFhaGvLw87Nq1C/v27cORI0fUjzJWr16N\nhIQEpKenazzyaPvoQxdjzpMhQ4bo3dfgwYMBQOfx9+7dW/37kiVLsHnzZgQHB+P++++Hu7s7kpKS\nsGrVKvzyyy84dOgQxGKxRhnLy8sxevRouLi4YP78+RCJRAZXMSYlJWHt2rWYMGECnnjiCZSVlanP\nVWPL8q9//Qsvvvgi5HI5Fi5cCHd3d+zfvx/jxo2Dm5ub3jK0d462XdfU1IQ//elPOHz4MHr16oXH\nHnsMrq6uyM7ORkJCAiZMmICwsLAOj7uyshLjxo2Do6MjHnjgATQ2NuL777/HkiVLIBKJsGDBAvVr\nGWOYN28efvrpJ0RERODZZ59FU1MTNm/ejAsXLnR4DIJJkyahqqoKmzZtQkxMDO677z71uraP3ZKT\nk7F+/XqDrgPNzc2YNWsWDh48iMjISDz22GOQSCT49ddf8eyzz+LMmTPYunVrh+UrKirCiBEjUFNT\ng5kzZ+L+++9HQ0MDrl+/jq+//hrPPvssPDw8NN7T+rjj4uLQp08fre2eO3cOu3btgrOzs8ZyU871\ntgz5bicnJ2PKlCmora3FnDlz0L9/f2RkZODrr7/G7t27cfjwYQwfPrzDz6c9e/fuBQCN7zUA/Prr\nrwCAe+65R+s9d911F5ycnJCUlITm5mbY29ura31TUlLUf+OUlBQALdeJS5cu4c0338TGjRuNryU2\nKox3QkcZ8+DBgzXu9ARVVVU6l+fn57OAgAAWFRWltY7jODZp0iSNZcJdsJubG7tw4YLGukceeYRx\nHMe+//57rbK1zR6EO2WO49iWLVs01n366aeM4zj217/+VWP5kiVLGMdxbMWKFRrL09PTmaOjo/rO\n2xBvvfUW4ziOzZ8/X2N5dnY28/Dw0JkxFxQUsKamJq1tHTx4kInFYvbUU0/pPEZdZaqsrGQcx7ER\nI0bovMNv/bcqLy9n7u7uzNvbm2VkZGi87sKFC0wmk7GhQ4dqLBfu5o29wzTlPNG3r/aOn7GWLGTe\nvHmsoaFBY118fDzjOI5t2rRJY7lwzixcuJAplUqDj6v1+fbZZ591uizZ2dnM3t6eeXp6ahy3SqVi\n8+bNYxzH6c2Y257vrY+t7fft1VdfZRzHsTlz5mide01NTay0tFT9//YyZo7j2LJlyzTOtUuXLjE7\nOzvWv39/jddv3bpVndk0Nzerl1dWVrLIyEid5dQnJydH53dJYMp1QDjO5557TuN4lEolW7p0KeM4\nju3evbvDsr3//vuM4zj2/vvva62rq6tj9fX16v939LcT5OXlscDAQCaVStmZM2e03m/Mua5Pe99t\nlUrFIiMjmUgkYt98843GOiE7jYyMNKpWgTHG3nnnHbZ69Wr2/PPPs/HjxzN7e3u2bNkyrXNy+PDh\njOM49vvvv+vczoABA5hIJFJfx5RKJRs2bBiTyWTs6aefZosWLWL29vZs1KhRjDG+tmjUqFEaWbYx\nrCYw79mzx+htPvvss4zjOJaXl6exvL3AvGrVKq3tCF+yl19+Wats+gLzhAkTtLbT3NzM7Ozs2IgR\nI9TLGhsbmZOTE5PL5ay2tlbrPcuWLTMqMIeFhTE7Ozt2/fp1rXXCF0XfxUSX6Oho1qdPH41l7QWm\nqqoqxnEcGz9+fIfbfu+99xjHceyjjz7Suf75559nHMexS5cuqZeZGpjbo+88MTUwx8TEMAcHB1ZV\nVaW1TqFQMC8vLzZy5EiN5RzHMYlEohGQDCGUpe0NjKllefPNN/U+Orl+/ToTiUSdDswKhYK5ubkx\nZ2dnVlRU1OExtheYZTIZq6mp0XrPXXfdxUQiEbt9+7Z62eTJkxnHcezEiRNar//f//5nVGDOzs42\nKDAbeh1QKpXMw8ODBQQE6Lwxq6ioYCKRiD3wwAMdlu2DDz7Qe6PWliGBubq6mg0aNIiJxWK2c+dO\njXWmnOv6tPfdTkxMZBzHsXHjxul874QJExjHcez48eMG7Uvg5+enUXU+fvx49ssvv2i9Ljw8nIlE\nIpaVlaVzO2PHjmUcx7HTp0+rlxUWFrIHH3yQeXt7qx/PlZSUMMb4GwKpVMoyMzNZeXk5e/TRR5mL\niwuTSCRs9uzZOh+dtmYVVdkcx2HkyJF61588eRKbNm1CUlISSktL0dTUpLG+oKAAQUFBBu1LV1WI\n8N6KigqDy6xrO3Z2dvD19dXYzpUrV9DQ0ICRI0dqVREBwLhx4/DFF18YtM+amhpkZWWhV69eCA0N\n1Vo/ceJEve/9+uuvsXnzZqSnp6OyshJKpVK9ztHR0aD9A3xjklmzZmHv3r2IiYnBvHnzMGHCBIwc\nOVKrJXxSUhIAvlVifHy81rauXr0KAMjIyEBUVJTBZdDHnOeJPnV1dUhPT4e3tzc2btyo8zUODg7I\nyMjQWh4SEgIvLy+T9qvr+2FKWX7//XcAus+V0NBQBAcHIy8vz6QyCi5fvozq6mqMHj26062Bw8PD\nIZPJtJYHBweDMYaKigr1eXf27FmIxWKMHTtW6/Xjxo3rVDn0MfQ6cPXqVVRUVCA8PByvv/66zm1J\nJBKd501bs2fPxv/93//h6aefxoEDBzBt2jSMHz8e/fv3N7r8SqUSDzzwAM6fP4933nkHc+fOVa/r\nzLluLOG8vPvuu3WunzRpEhITE5GWloYJEyYYvF2he1JpaSlOnjyJFStWYNq0adi8ebO60Zap/P39\nsW3bNq3l165dw+rVq/HGG2+gb9++uO+++3Ds2DF89NFHcHFxwTPPPIO5c+fi9OnTerdtFYEZAHx9\nfXUu/+GHHzB//nxIpVJMnToVffv2hbOzM0QiEY4cOYJjx46hsbHR4P3oel5pZ8d/DK2DlSnbEbbV\nejtVVVUA9B+fvuW6dLQtfRfB5cuXY9OmTQgICMC9996LwMBAODk5AeBbyxvbr/C7777D+vXr8c03\n36hb4EskEsyfPx/vvvuuun3ArVu3AACff/653m1xHIfbt28btX9dzH2e6CNcbEtLS/VeYAHdzzI7\nE6R0vdeUshhyDnU2MFdWVgIAAgMDO7UdoP3vGQCt75qnp6fOAVKM+Z6Zq3ytyyZ8F65du6b3b2Xo\nd6FXr1747bffEB8fj/3792PXrl0A+JuVl156Cc8++6zB5ReC+5NPPokXX3xRY11nznVjCeelvh4S\nwnLh3DKWt7c37rvvPgwdOhQRERF48cUXNQKzm5sbGGPqcugrX0ftXRhjWLp0KQYNGoTly5fj2rVr\n2LNnD9588031/mpqarBgwQIcOXJE70BYVhOY9Vm1ahUkEglSUlLQr18/jXUFBQVWP1qQ0FVBX6f2\n9jq7tyU0zNH3nuLiYq1lN2/exPvvv4/o6GicOnVKK2v/3//+Z/D+BRKJBKtXr8bq1auRn5+P48eP\nY/Pmzfj666+Rk5OD48ePa5T33LlzGDhwoNH7MUZ3nSfCMQ0dOlTd2MNQnbmA6XqvKWVpfQ7pqqXQ\ndQ4JgU6hUGit03WhFC5eBQUFBpXJXFxdXVFeXg6VSqUVnI35nnUF4XOfO3cuduzY0entRUZGYtu2\nbVAqlUhPT8fhw4fxwQcf4G9/+xucnZ0N6pqzYcMGfPbZZ5gxYwY+/PBDvWU25Vw3lrAvXecf0JL5\nttc40RC9evVCVFQUzp07h5KSEvUNW79+/ZCamoorV65oNfBTKBTIzs6Gvb29zkZzrX344Yf47bff\nkJaWBo7j1LUJQver1r9funRJb2C2+vEmMzMz0b9/f62LrUqlQmJiooVKZbioqChIJBKcO3cOtbW1\nWuuNOQYXFxeEhYUhPz8f169f11qvq1X29evXwRjDtGnTtIKyvu0ILSwNqUEICgrCI488ggMHDqBv\n375ITExU32mPGTMGANSB2hDG7Ls1c54n7ZVBJpNhwIABuHDhglGPPrqCKWUZNmwYAP3niq5sWS6X\nA4DOmhVdF+yoqCi4ubkhPT3d4JGOzGHo0KFQKpU4efKk1jpzngOmiIqKUrdm1nWDYyqxWIyhQ4fi\n73//O7799lsA/EAZHdmxYwdWrFiBmJgYfPfddzpv/Mx9rrf3mQrB6siRIzrfKyxvHeBMVVhYCI7j\nNB6RCD1J9u/fr/X648ePo76+HmPHjoW9vb3e7ebk5ODVV1/FP/7xD0RGRmqsa11b19DQ0GEZrT4w\nh4aG4urVqxpfcMYY4uPjkZGRYfUj89jb2+Ohhx5CZWUl3nzzTY116enpBnWPaG3x4sVQqVR45ZVX\nNDrqZ2dn4/3339d6vfAs+sSJExrDytXW1mLZsmU6vyTCYA+5ubla68rKynD+/Hmt5bW1taitrYW9\nvb26G8/ixYvh7u6ONWvWIDk5Wes9KpVKK0C0t+/2mPM86agML7zwApqamrBkyRKdVV8VFRU4e/as\nUeU3lbFlefTRR2Fvb48PPvhA4/hUKhVefvllnYM/jBgxAiKRCN98843GSHjl5eX4+9//rvV6kUiE\np59+GvX19XjyySe1nvU3NTWhrKzMpONtj9B1auXKlWhublYvr6qqwhtvvGHUtoSbEWPPQ33EYjGe\nffZZFBUV4bnnntN5cS4qKjLoee3vv/+u828tZJsdjXqYlJSExx9/HEFBQdi3b5/Oti8Cc57r7X2v\nxo0bh379+iExMRE7d+7UWLdjxw4kJiaiX79+GD9+fIf7uXbtms6yqlQqvPbaaygtLcWUKVM0jnv+\n/Pnw8vLCtm3bkJqaql7e0NCAlStXAgCeeuqpdve7bNkyREREYMWKFeplAwYMAADs2bNHvUzosiWs\n06Vbq7J1fenbWw7wz0effPJJDBkyBHPnzoW9vT1OnjyJjIwMdSOkrtRe2Qy1bt06/Prrr9iwYQPO\nnDmDMWPGoKioCNu3b8fMmTORkJBg8MQBL774IhISErBz504MHToU06ZNQ2VlJbZv34677rpL4wQA\n+GdrDz30ELZt24aYmBhMnToVVVVVOHToEKRSKWJiYpCWlqbxnsjISAQGBmLbtm3qPnscx2HBggUo\nLy/H0KFDER0djejoaAQHB6O6uho//vgjSkpK1FVpAODh4YEdO3YgLi4Oo0ePxuTJk9G/f39wHIe8\nvDwkJSWhoqICdXV16n1PmTIF7777LpYtW4a5c+fCxcUFcrkcTz/9dLufiznPk/aOv1evXli8eDFS\nU1Px0UcfoW/fvpg+fTqCg4NRXl6O7OxsnDhxAkuWLMFHH31k8D5NZWxZevfujXXr1uHFF1/EkCFD\n8OCDD8LV1RUHDhxAdXU1Bg0ahHPnzmnsw8/PD48++ij++9//IiYmBjNmzEB1dTV+/vlnTJw4Uev8\nAfh+q2fOnMHevXsRERGBmTNnwsXFBXl5eTh06BDeffddjT7I5rBgwQJs27YN+/fvx8CBAzFr1iw0\nNzdj165dGDFiBK5evWrw90wmk2H06NE4ceIEHnvsMYSHh0MsFmPOnDmIjo42qXyrVq1Ceno6Pvnk\nE+zduxeTJk1CYGAgbt68iWvXruHUqVN4++23O2wIuXXrVnz22WcYP348+vTpA7lcjqysLOzduxcS\niQTPP/98u+9funQpGhsbMXLkSHz66ada6+VyOf72t78BMP78ak9H3+0tW7Zg6tSpePDBBzFnzhz0\n69cPV65cQUJCAlxdXQ1OYvbt24dXX30VEyZMQEhICDw9PVFSUoJjx44hOzsbvXv3xieffKLxHhcX\nF3z++eeYP38+YmNj8dBDD0Eul2PPnj24evUq7r//fjzwwAN69/nFF1/g2LFjSE5O1jjH+vbti7i4\nOHz11Veora2Fi4sLtmzZglGjRmn1pdZgVNvzTggJCdHZXSo2Nlare0ZbmzdvZjExMczZ2Zl5e3uz\nuXPnsgsXLrD4+HgmEol0drNo2y1C32sZ0981QlfZOupKExISwkJDQ7WWFxQUsIULF6pH/hoyZAjb\nunUr27Fjh1F9ARnjuze88MIL6pG/oqKi2MaNG9n169d1HkddXR177bXXWFhYGJNIJKxXr17smWee\nYbdu3dL7+ScnJ7PJkyczNzc3dReaY8eOscrKSvb666+zu+++mwUGBjJHR0cWEBDAJk2axLZt26az\nvDk5OeyZZ55h4eHhTCKRMDc3NxYVFcUWLFigs9/mxo0bWVRUlLqPt67PUxdjz5NFixbpPCfbO/7W\nfvzxR/anP/2J+fj4MAcHB+bv789GjRrFVq1apXNUM0O76rTW0flmSlkYaxn5SyKRqEf+Kioq0ns+\nNDY2spdfflk9elt4eDhbt24dUygUeo9NoVCwf//732zkyJFMJpMxZ2dnFhERwf7yl79odEsx5nss\n0Pe3a2hoYP/4xz+0Rv4qKChgHMexuLi4dj/H1jIzM9msWbOYp6en+hwQuh2Zeh1gjLH//ve/bPLk\nyczDw4M5ODiwoKAgNmHCBLZ27Vqtkfh0OXPmDHvqqafY4MGDmYeHB3NycmLh4eFsyZIl7OLFixqv\n3bx5s0a5hbKJRCK9o3HpKrex55c+HX23r1y5wh5//HHm7+/P7O3tWUBAAHv88cfZ1atXDd7HhQsX\n2DPPPMNiYmKYl5cXs7OzY3K5nI0ZM4a9/fbbOrvfCU6ePKkeXc/JyYkNGjSIvffee+32n87Pz2fu\n7u46u+IyxvejX7hwIXN3d2cymYzFxcWxwsLCdo+BY8y6B4NdsmQJ9u3bBx8fH3UVanx8PL744gt4\ne3sDANauXatzxBZb8Nprr2Ht2rU4cOCAehQwQoh5HTp0CNOnT8err76Kt956y9LFIaRdVh+YT5w4\nAZlMhgULFqgD85o1a+Di4oIXXnjBwqUzXGFhoXrYS8H58+cxduxYSCQSFBQU6Bx0nRBiuKKiIq0u\nN7du3cK0adOQlpaGM2fOdHpYR0K6mtV3l5owYQJycnK0llv5/YSW4cOHIzw8HAMGDICzszOuXbum\nnnbw888/p6BMiBksX74c586dw5gxY+Dt7Y38/Hz8/PPPqKiowJNPPklBmdgEqw/M+nzwwQfYunUr\nhg8fjn/+858GTXRgSU8++SQSEhKwbds21NTUQC6X495778VLL72Eu+66y9LFI6RHmDdvHm7evIkf\nf/wRlZWVcHJywoABA7B06VLjpt0jxIKsviob4PuHzZo1S12VffPmTfXz5VWrVqGoqEhjbk1CCCHE\nVtlkxiwM+QgATzzxBGbNmqX1mrCwMGRlZXVnsQghhFi5vn37IjMz09LFaJfVDzCiS+tBJH744Qed\n/QqzsrLA+Nmz7uif1atXW7wMlv6hz4A+A/oM6DMQfmwhYbP6jPnhhx/GsWPHUFZWhuDgYKxZswZH\njx5Vj0UaGhqqs5M8IYQQYousPjAL47+2Ro04CCGE9FQ2WZVNDNfusG93CPoM6DMA6DMA6DOwFTbR\nKtsUHMehhx4aIYQQE9lCbLD6qmxCCCHm5+HhYfGpS7uSXC5HeXm5pYthEsqYCSHkDtTTr5H6js8W\njpueMRNCCCFWhAIzIYQQYkUoMBNCCCFWhAIzIYQQYkUoMBNCCCFWhLpLEUIIMVhmeSYOZB5Afk0+\n+sr7Ynrf6Qh2C7Z0sXoU6i5FCCF3IF3XSIVKgeSCZCTeSIRCpcCY4DEYEzQGjnaOAIDUwlR88NsH\ncLJzgsxBhurGaiiYAq+MewURnhHq7TQpm5BfnQ8HsQMCXQLBcVy3Hhtg292lKDATQsgdqO01UsVU\n+DTlU5zKPwW5RA4OHCoaKjDAewBeGPMCOI7DSwdfgp3IDjIHmfp95fXlkEvkiI+NB8dxOJ1/GlvT\nt6JB0QAwINA1EE+NeAoBLgEGl62qqgqJiYmYOXOmxvKRI0di9+7d8Pf3N/r4OlpuTegZMyGEEFy9\ndRWnC06jj3sfeDh5QO4kR6h7KC6WXsTZorMovV2KmsYajaAMAHKJHDeqbuB2821klWfhk5RP4OLg\ngl5uvRDsFozy+nK8e+pdNCoaDS7LL7/8ghkzZgAAUlNT1cvj4uIgEvX8sNXzj5AQQkiHLpVegr3I\nXqPameM4yBxkOFt8FhI7CVRQaWWbSqaEWCSGvcgev2b/CkexI5zsndTv93b2Rnl9OS6WXjS4LBzH\nqcuxfv169XIPDw9IJBLs2rULa9eu7czhWjUKzIQQQuAodoRSpdRarlApILWXQu4kR7RPNAprCtXr\nGGMoqC7A+F7j4WjniOLaYjg7OGttgzGG6sZqg8ty9uxZAMChQ4fg4uICANi+fTsCAgLg5uaGYcOG\noampydhDtBkUmAkhhGBYwDBwHKdR5dysbEajohGjg0YDABbHLIa/iz9yKnOQW5mL3Kpc9PPqh/v7\n3w8AiPSKRFVDlcZ2GWPgOA7+so6fCwtEIhFCQkJw6NAhNDQ0oE+fPigsLMSsWbPMcKTWjxp/EULI\nHUjXNfJCCYUiAAAgAElEQVR47nFsTtsMlUqlfs28/vMwM3ymumpZqVLiWvk1VNRXwNvZG33lfdXr\nyurKsPrIaiiZEj7OPlCoFCisLkSUdxReHvcyRJx5csHc3Fxs3rwZq1evNur42ltuTagfMyGEEADA\nXb3vwkCfgcgozYCKqdDPqx98nH00XiMWiRHpFanz/V5SL/zfhP/DzoydOFt0Fg5iB9wbcS9mR8w2\nW1AGYPWBtbMoYyaEkDtQV18jlSolRJzI7H2Ya2tr8emnn+LYsWN4++23MXDgQJ2vs+WMmQIzIYTc\ngXr6NdKWAzM1/iKEEEKsCAVmQgghxIpQYCaEEEKsCAVmQgghxIpQYCaEEEKsCAVmQgghxIpQYCaE\nEEKsCAVmQgghxIpQYCaEEEKsCAVmQgghxIpQYCaEEGKc+nrg++8BhcLSJemRKDATQgjRdOUKkJqq\nf/2JE8C33wJnz+p/TX098Mf0kcQ4FJgJIYS0UCqBzZuBr74CGhq019fXAwkJgL8/sH277qyZMeBf\n/wIOHjSpCFVVVdi3b5/W8pEjR6KoqMikbdoSCsyEEEJapKcDhYVAbS2QlKS9/sQJoK4O8PEBbt7U\nnTVnZAAXLwK7d/OvNdIvv/yCGTNmAABSW2XucXFxEIl6ftjq+UdICCHEMEolnwXL5Xzg3blTM2sW\nsmVfX/7/Hh7aWTNj/Ps8PPj3Hj9udDE4jlPP47x+/Xr1cg8PD9TU1OCHH37AmjVr8Pvvv5t0mNaO\nAjMhhBCekC27uwNSqXbWLGTLTk78/11dtbPmjAwgM5MPzL6+JmXNZ//Y3qFDh+Di4gIA2L59O/z9\n/bF3714EBgbihRdewLvvvtupw7VWFJgJIYRoZsuC1llzQwOwaxfQ2Ajk5rb81NXx71OpWrJlV1eA\n4wCJxKSsWSQSISQkBIcOHUJDQwP69OmDwsJCzJ49G8uXL8fIkSORl5eH0NBQM38I1oFjjDFLF6Ir\ncByHHnpohBDSaVrXyN9/B958k890WysrA/72N2DcOCAxUXdjLwcH4K67gMuXgfXrgZAQPjADfGCu\nqgL++U8+CzeTt956C8uXL4dUzzb1xQBbiA12li4AIYQQK+DmBjz2mO51Pj588L377va3kZTEZ855\neZrLVSq+invYMLMUdc+ePXjuuedQUFCA8PBws2zTmlDGTAghd6AuuUY2NfFV3bo4OwNmaFH9ww8/\n4O2334a7uztiY2Px2muv6XydLWfMFJgJIeQO1NOvkbYcmKnxFyGEEGJFKDATQgghVoQCMyGEEGJF\nKDATQgghVoQCMyGEEGJFKDATQgghVoQCMyGEEGJFKDATQgghVoQCMyGEEGJFKDATQgghVoQCMyGE\nEGJFaHYpQgghBlmxAigu1r3Ozw9Yt657y9NTUWAmhBACQH/gFYJucTE/1bIuOTmGbcMQVVVVSExM\nxMyZMzWWjxw5Ert374a/v79hG7JRFJgJIYQA0B94haDbXdv45ZdfEBcXBwBITU3FsD/mcY6Li4PI\nDFNHWruef4SEEEJsCsdx4DgOALB+/Xr1cg8PDzQ0NGD79u1Yu3YtUlNTLVXELkWBmRBCiFU5e/Ys\nAODQoUNwcXEBAGzfvh3+/v44efIkPD09ER4ejqtXr1qymF2GAjMhhBCrIhKJEBISgkOHDqGhoQF9\n+vRBYWEhZs+ejUceeQShoaFISUnBvHnzLF3ULkHPmAkhhBjEz0//s2I/P/PtJz4+HvHx8XrXh4aG\n4r777kN8fDzefvtt8+3YSlBgJoQQAkB/4BWCrjV0h3rllVewcOFCODo64sqVK5YuTpegwEwIIQSA\neQJvR8G9s+677z5kZmbi4sWLeP31182zUSvDMcaYpQvRFTiOQw89NEII6bSefo3Ud3y2cNzU+IsQ\nQgixIhSYCSGEECtCgZkQQgixIhSYCSGEECtCgZkQQgixIhSYCSGEECtCgZkQQgixIlYfmJcsWQJf\nX19ER0erl5WXl2Pq1KmIiIjAtGnTUFlZacESEkKI7ZHL5epZnHrij1wut/RHbDKrD8yLFy/G/v37\nNZatW7cOU6dOxdWrVzF58mSss4Zx4gghxIaUl5eDMdZjf8rLyy39EZvMJkb+ysnJwaxZs3D+/HkA\nQGRkJI4dOwZfX18UFxcjNjYWly9f1niPLYzuQgghpHvZQmyw+oxZl5KSEvj6+gIAfH19UVJSYuES\nEUIIIeZhk4G5NeF5AiGEENIT2OTsUkIVtp+fH4qKiuDj46Pzda3n84yNjUVsbGz3FJAQQohVOHr0\nKI4ePWrpYhjFJp8x//3vf4enpydeeeUVrFu3DpWVlVoNwGzhOQIhhJDuZQuxweoD88MPP4xjx46h\nrKwMvr6+eP311zFnzhw88MADuHHjBkJCQvD999/D3d1d43228OETQgjpXrYQG6w+MJvKFj58Qggh\n3csWYoPNN/4ihBBCehIKzIQQQogVocBMCCGEWBGb7C5FCAFWrAD27QPq67XXeXkBp093f5kIIZ1H\ngZkQG1VcDIjFQFCQ9rr8/O4vDyHEPCgwE0I6ZcUK/iahLT8/gOaXIcR4FJgJIZ1SXAyEhGgvz8np\n7pIQ0jNQ4y9CCCHEilBgJoQQQqwIVWUTYqP8/AClUndDLy+v7i8PIcQ8KDATYqPWraPGVYT0RBSY\nCSGd4uenu6GXn1+3F4WQHoEmsSDEwqi7ESHdxxZiA2XMhFgYdTcihLRGrbIJIYQQK0KBmRBCCLEi\nFJgJIYQQK0LPmAnpwfQ1LAOocRkh1ooCMyEW1pXdjfQ1LAOocRkh1ooCMyEWZq6sVVd2nJgIZGYC\nU6Z03T4Ayr4JMScKzIT0ELqy47Q0oLa2a/cBUPZNiDlR4y9CCCHEilBgJoQQQqwIBWZCCCHEitAz\nZkJ6MJmMfy5Mk0wQYjsoMBPSQ+jqdhUWBowfTy2mCbElFJgJ6SG6I/jSFI+EdD2a9pEQQsgdwxZi\nAzX+IoQQQqwIBWZCCCHEilBgJoQQQqwINf4iPRaN60wIsUUUmEmPReM6E0JsEQVmQrpYT5oTmWoh\nCOl6FJgJ6WI9aU5kqoUgpOtR4y9CCCHEilDGTEgPR9XPhNgWCsykx6LhI3lU/UyIbaHATHqsOzEb\n1JUdJyYCmZnAlCmWKRMhxDgUmEmPZslqXGHfqal8cBQ4OQHDhrWUw5x0ZcdpaUBtrXn3QwjpOhSY\nSY9myWpcYd9t979zp+ZrFi1q+b+1P/elxwOEdD0KzISYgb4q5LQ0QCbTrEaurzd/9ylh/8I+BTKZ\nadvTx5pvGgjpKSgwE2IG+qqQ3d2Bysru27+wT0F37Lur9aQBWggxBAVmQtCzuxTJZPyxtc3GbaX6\nuScN0EKIISgwE4LuexZ9+DBQUgIkJGgub1vdbU5TpvDHsXlz12y/s3ryTREhpqDATHo0SzZWksn4\nquTa2pYyFBfzrbJbVzcD5qtyFvYpEPZtzdkx9bMmRBMFZtKjWTLjEjLg1tnqokV8n+K2gVhfADX2\n+WrbrNuaM2VCiG4UmAkxA32ZubCuNV1V1voCqKHPV42tGaDq425SWMh/qCKaloAYjgIzIWZg6WBm\n7P6p+rgbVFQAb7wBLFsGDB1q6dIQG0KBmRBi1YypjbAqhw4BZWXAjh3A4MGAWGzpEhEbQYGZEHRf\nIzEaOct4xtYGWEU1fUUFcOAAEBEB5OcD6emUNRODUWAmBN13wbZ0lXdrhw9rj6FdW8sHto7Kac7g\nZ+6bFauopj90CGAMsLcH5HLKmolRKDATYoXaG2LTXP2da2u1u20B+luBt32NuYKfNd2smIWQLfv7\n8/93dweysylrJgajwEyIFRICX2amZlbbegSvzlR/+/npnnHK3GNr35EOHQKUSr4ltlLJL3Nzo6yZ\nGIwCMyFWzFz9ktvrD902C+8o612xQjuTF7YTFmZ82Xqcq1cBBwd+iLfWxGK+MZivr2XKRWwGBWZC\n7gDmnGSjuJgPwl01epnNW7nS0iUgNo4CMyHEOjEGqFRGV/1Sy3di6ygwE2KknjINoa6xvAHDAljb\nMbkBfjtmDX4nTgDnzgFPPw1wnMFvs5XPnxB9KDATYqTumIawO7I+XWN5G/ve1nJyzBgUGxv5xlLl\n5cDMmUBoqJk2TIj1o8BMiBW647O+pCSguppPzRMSgOefNyprJsSWUWAmpAu1rvZOTQXq6/nfnZyA\nYcP437uj+rttBt66LFVVmq2phbIZWi5hoJLaWn72rNb7NOm4GhuBXbsAHx++MOnpfOEpayZ3CArM\nhHSh1tXeaWlAUBD/e2Vly/LOVn+vWAHs29cSaAXtBdhFi1r2n5Cg2cJaKFt741O3Xie00vbz06zi\nN/m4hGzZw4P/v0RCWTO5o1BgJsTGFRfzDZeFoC/oKMCaqr0g32mts2WBry9lzeSOQoGZECPZ7GxH\ntuD8eb7Bl6MjcOtWy/KmJuDYMQrM5I5AgZkQI/W0hlmFhZrdv5qa+JpjYTTJbjVokP4P2NW1e8tC\niIVQYCbkDqdQaMc8d3d+tsJu5+CgXSdPyB2GAjMhXah1tbdS2RLsnJzMMxmFOcqlUvFtrQR2dvzz\naScn47fVdjkhxHgcY4xZuhBdgeM49NBDI0TDihX8WBytZ4YqLOT/DQjguzGNH9+yrm0rbX2Nt0yd\nMIMQa2YLsYEyZmLVyuvLcaPqBqT2UvSV94VYdGdPmadvOFAnJz4jF/pG19a2BGpd3Zhab6f1TFHm\nnO+ZEGIamw7MISEhcHV1hVgshr29PX777TdLF4mYCWMMOy/txL5r+8CBAwODt7M3/jbqbwh0Dez2\n8ugLiMYMotGZbQjvTUzUzIyFQCp0ixIy3I66MO3b1zI3RHl5y9zMTU38vxScCbEcmw7MHMfh6NGj\n8BAGIiA9RnJBMhKuJCDEPQR2Iv40Lasrw3un38PaKWvVy7qLvvGxjekjrGsbhw/zwbZtwG4brIX3\nClM1CoydavHwYX5bJSWAszO/rLGR/3FxadlXTo7mM2Jz3Jh0i5oa/sBEIkuXhBCT2XRgBmD1zwqI\nafZn7YeX1EsjAHtJvZBblYtrt64hyjvKgqUznTB8peDyZb7x1U8/8c+DBUpl1wQ8oYq7vJwfUAvg\n/21oACIj+UAfE6P9bNkcNyZdTqEA1q4F7r0XmDDB0qUhxGQ2HZg5jsOUKVMgFovxl7/8BcuWLbN0\nkYiZVDVUQWIn0V7BgNvNt7u/QGZSW6uZ8XIc30NIJGpZfv06UFGhOe50YiKQmdmtRbU9KSn8h7dz\nJzBqFP/BEmKDbDownzx5Ev7+/igtLcXUqVMRGRmJCXSn3CMM8h2EE7knEOTW0qdVxVRgYOjl1suC\nJTPNihV8cC0v1x7MQyTSfG7c1ATY22tmqGlpmpl2R/R1YTL7nMnWQqHgm6YHBvIjhp05Q1kzsVk2\nHZj9/f0BAN7e3oiLi8Nvv/2mEZjj4+PVv8fGxiI2NrabS0hMdU/YPfit4DcUVBfAS+qFRmUjSm+X\nYlrfafBx9ul4A1ZGmOihtralChngM+aueBqjrxpcaBR26ZL592kssz63TkkBysr4g+M4ypqJ2tGj\nR3H06FFLF8MoNhuY6+rqoFQq4eLigtu3b+PgwYNYvXq1xmtaB2ZiW3xlvlg1cRX2Xd2H9JJ0uDq6\nYtmwZRgXPM4i5bH0IBoyGR/EnJw0R+TqaKCStsFP6BrV3MwPKlJXxz/jVqn4qSDt7Pjn2ytWdH2j\nLrM9txayZU9P/v8yGb8RypoJtJOyNWvWWK4wBrLZwFxSUoK4uDgAgEKhwKOPPopp06ZZuFTEnPxk\nflg6dKmliwHAPEFKJmvpjtRWZSVw7hz/e20tX72dkNDyvilTTBvwo23wy8zkt+/ryw86InS/att/\nuW1wtPSNSbtaZ8sCLy/KmonNstnAHBoaijRhVARCbIAQ+Fo/K1ap+HmUxWKg1x+Pzm/c4AOl0BjM\n2C5RhpRBCPKGTtloVV2i2jp9mn8ecOOG5nKO4xuDRUZaplyEmMhmAzO5M5XeLkVyYTIq6isQ4RmB\nwX6D4SC2zoxI3+hagGaGKmSiQoAUulQJAbm2VrtfsaXpez4MdK5vs9DPunWL9A63+fTTfHW2LhId\nLfsJsXIUmInNOFd8Du//9j5UKhXsxfY4mHUQfeR98NLYl+Ds4Gzp4mlpXY1szMAgbUfdssYxq/U9\nHwY617dZ6GfddtvtbtPenv8hpIegwExsQqOiEZ+kfgK5RK4RhK9XXseBrAOYGzXXgqXrmEymGYyF\nLBjgs0F92Sch5M5DgZnYhKyKLDQoGrS6Svk5++F47nGrD8wdZcFtq25tQdtRzAS1tYa16tbVoKzH\n9rMmxAgUmInt0NHf11xTuDUrm3Gt/BoaFY0IcQ+B3Ene6W1ag7bBLzWVb2xWVQWEhfGNmZVKfp2d\nHRAUxM9QZUhwrK3lB0xp29K8qYnvvQS0H5x1rTO0MRohPRkFZmIT+sj7wMHOAXXNdZDaS9XLS2pL\nMCN8Rqe2nV2RjU1nNqGqoQocxwEA5vSbg9n9Zqv/39X0dUcS1pmqbfATAl9CguZ42QAfUOvrjWu8\n1dSku32V0O+aEGI8CszEJkjsJFg2dBk+TP4QHDg4iB1Q31yPYLdg3BN2j8nbbVA04F+n/wUOHHq7\n9wYAKFQK7Li0A73deyPGL8bkbRsTbE1txdy2dXRqKj8ACWN8V97WvLw0ew7pCqqGBlQ/Pz5j1tUv\nuzPdhq26vzQh3YQCM7EZwwKG4a2738Lp/NO4VXcLUd5RGBYwTPdkFwbKKM1ATWONOigDgJ3IDm4S\nN/xy/ZdOBebu6PvbtnV0Whrg6Mj/HhSk+drWI4Z11rp1/L7btjYXmNr32uyfmULB19ETYkPojCU2\nxd/FH3FRcWbb3o2qGyitK4WzgzM8nDwg4vh5fCV2ElQ0VJhtP4TXrfM6X7oEfP89sHIlBWdiU+hs\nJVapprEG+67tw4kbJwAA44PHY2bETLg6uppl+wqVAlvStuBA1gFcLruM/Op8ONs7Y0zwGMgcZCiv\nL+/0s+uebMUKvto8J0e7C7GdHRARoft93TavM2PA9u38OKcpKcDo0WbeASFdhwIzsTqNikZsOLUB\n+VX58JPxDxcPZh3EhdILWHXXqk5VXQuOZB/B0Zyj6OfZDw2KBuRU5qC2qRan8k4h0jMSrhJX3B16\nd6f3090KC4GaGn6SijNnNNcplcDly/zvup4PG/NsuLgYmDdPd5ep2lq+xbdFnwtfusQPxxkSwjcR\nHz7c9rLm8nL+poLmALjj2NiZSu4EacVpyKvKQ4h7iHpZb/feyKnMQWphKsb16vwMUweyDsBP5gex\nSIwYvxh4OHngesV13Kq/hYG+A7Fw8EJ4OHl0ej/moq8KODVVMwNVKPhxt5ubW541C+rq+MZfmzfz\n29u3j2+F3ZpSaVxAbds/G7CCkcoY44Oxqyv/k51tm1nzvn38T1QUEBxs6dKQbkSBmVidK7eu6MyK\nneyccOXWFbME5prGGng7ewMARJwIIe4hCHEPQV5VHu4JuweeUs9O78Oc9FUBC9XJAsb44MwY0Nio\n+drWCeO6dVY+MYUuhjbkap0tA3xzdFvLmsvKgF9/BZydgT17+PHAyR3DRs5ScifxdPJEk1K7H06T\nsgleUi8d7zDeIN9BOFdyDv4u/uplzcpmcByHYFfbyU6GDWvJTles4Kcklsn4iZaEqmkHB6BPH82W\n0l01CYW5qcvZ2AhcuAAMGABIJPrL2DpbFvqgu7jYXtb888981UdAAJCcDOTlUdZ8B6HATKzOqKBR\nSLicgNqmWsgcZACA2023+XWBo8yyj/si78P5m+dRUF0AT6kn6prrUFFfgQcHPggXRxez7KO7FRe3\nTBd57VrLM2Slkv+9qalluExzTkLR9jlzTg7/jNnJib9xaO3gQUAqhZa2fa5bH1NICICLmUDjFaDJ\nHogcpL+Mubl8AUQivu5eoFQCv/xiG4FZyJYDA/njcHSkrPkOQ4GZWB0vqReeG/UcPk39FOX15QD4\nauznRj0HX5mvWfYR6BqI1RNX4+fMn3Hx5kX4ynyxcPBCDPUfapbtW5pKpfmMWRhIRCZrebbcdjrz\n1lNRdqT1QCDCDYHAwYHvQ11ZqR38pVLgsce0t9fuzUB9PZCZCfj48JlvWBgAHdEd4Ce1XruWz5zb\ncnJqZyc6VFbymWp0tHHvM0ZVFV/W1p3BhWxZqHb39aWs+Q5DgZlYpWjfaGycvhHZFdkAgFB5qMnz\nLhdUF+Bi6UVw4DDQZ6C6+trfxR9LhiwxW5mtgUzGP2IVMmSAv+4XFvLX+bAwzcy6NWMGBWldjdx2\nfOuEBFNLr8f16/y/dnZ89XRmJuA6SPdrRSLzNQffswc4fhz417/46nBzYwz4z3/4359/nj+2sjJg\n/37+7qagoOW1NTXA3r3AX/9q/nIQq0OBmVgtB7ED+nn1M/n9jDHsvrIbCZdbIgUHDg8MeAD3ht9r\njiJ2mrkH3Jgyha9WFolasuSGBr41dmUlv/7rrztX5m7V2MgHYiEll8n4rLlfBIDOd5vTq7QUOHqU\nb3D266/AnDmd3yZj/F1TVBT/B8rJAdLT+XU5OUBoKJ8pz5+vO+P37ESDRBoBzabQX4r0WFkVWfgh\n4wcEuwXDTsSf6s3KZmy7uA0DfAagl1svC5fQ8AE3zDmGdFkZ30W29Q1BTQ3/GDYhgQ/sraehtGhj\nsMJC/l+xmP9XJOIzy4ICAH27br8//8zvKyiIr/u/++7OZ805OcA77wAvvMBXjycktNw9JSTwWbNc\nDsSZb2Q7APxNxsaNwCuv6B4/lVgdCsykx/qt4Dc4iB3UQRkA7MX2EHNipBamWkVgNpQhgVEI3m0H\nD2k7cIhSyTdabv0alYr/EbbT+mbB7KNyGaqxEX4115BT4wnUtprli3nAj2UCdf66W5J1lpAtBwTw\nWaY5smbG+ODb0MCPSObszGfLvf8Yoz09vSVrNreff+ZbtB86BNx/v/m3T8yOAjPpsZqUTRCLxFrL\nxZwYjYpGHe+wbULwXrSIr/1t3VK6spL/f04On3z26aP53suX+UB9333G71cjm6+thbLBEfn59nBy\n0g7qXl5GZP6Ojlh3YAg/WkpbdnYdN+ZSqfis11hCtixU/fr5dT5rzsnhW9v168dXxX/yCZ8tC126\nJJKWrNmcU40KNxkDBvDPrqdOpazZBlBgJj3WEL8hOJJ9BIwx9bzKjDE0KZsw2G9wl+yzqyZpMHa7\n7Y3IFR2t3dCrqcn0R5Dq/atUwKq1wGAJP3GEOQKMt7dp72tuBtavBx591LgstLSUzyxdXfn6fkFl\npelZs5AtS6X8ZyIS8UHyT39quenw8ADOnjV/1izcZEgkfDkoa7YJFJhJt6uor8DB6weRUpACZwdn\nTOkzBWOCxujMbjtjoM9ADA8Yjt8KfoObxA2MMVQ3VmN8r/GdalTWHlMmaahurMaNqhuoa66Dt9Qb\nga6BADTrn43ZbkfPo4cN096WWVpSp6fzXXo4jk/Bo6LMsFETJSfzA4o4OfHPdA25Saip4QPX8OEt\n9foCoWGWKYRsWfjQhY7l169rNuhyceGXmSswt66SBwB/f8qabQQFZtKtKhsq8cbxN1BZXwkvZy9U\nNVTh05RPce3WNSyKWaTObE3VpGxCRmkGqhqrEOASgCeHP4lRgaOQlJ8EESfCuF7jMNh3sHp6R0sr\nu12GtOxEiDgR7ER2KKwpRGZFJnqziWgbnA1lkYZaKhU/4paHBx94duwwX9ZsrOZmfv99+/KzS2Vn\na9fd63L4MLB7N59p6xt9pSOM8c9zBw7kj13IloGWAU/8/IAJE/hAuWaNadXthmhbJW9vT1mzjaDA\nTLrVr9m/oqK+Ar3d/2j0Yg+4OLrgWO4xTO07FUGuQSZvu6imCP9M+ifK6soABoDjs+ZnRj6DkUEj\nzXMARmhWNiOrIgu5VbmoKpZj56U8TA+brh7NrEHRgAKWAkV5EOxEdhDaYpU01cIj5BqAAd1WVpmM\nz8pNbvl97hyQn89ne4zxD7ktlTUnJ/PV0CEhfDD84YeOs+bqauCnn/hGWcKzXlNkZAD//Cfw6qv8\n82Slkr9p6dWmoaG/P/+h19Vpjs5iLnV1wOnTfMO1vLyW5SoV3zd79mztWU6I1aDATLpVWnGa1qxN\nIk4EDhxyK3NNDsyMMXyc8jHqmuvUs1IxxnC+5Dx+vPIj5g+Y39miG0WpUiIpLwm36m9B5iCDiBNh\n79W9SCtJw2sTXoPEToLsimwMeex79HLXvGg3KZtQ1VAF4MMuKZuuqu6wMGD0GAUWvpQBJVOir7xv\nh0OTqp97MwacVQGKp4F0B/jJarFuyHeWyZqFbFmoIvbxMSxr/uUXPogGB/PVzjk5xmfNjAE7d/J9\nr3fu5IOznR3w4oumHo3pnJz4bFyh0F5nb09B2cpRYCbdys3RDeX15XCB9kXfyd7IIRNbKagpQF5V\nnkYXKI7jEOgaiMPZhzGv/7xOV5MDQFldGeqa6+Dr7AtHO/0Xt5u3b+JW/S24S9zBcRzsRHYIcQ9B\nTkXL1JUiTgToKJKKqcz+vL01XVXdF0ou4MPkD7ExqQEAIBaJ8Vj0Y4gNjdW7HfVz7+ISQJkFuPJ/\n05xyD/557MWL3Z81t86WAf6mQCptP2sWsmU/P3596xbSxsjI4GsKIiKAK1eAq1f5rNkSOM70hnPE\n4kwOzIWFhTh16hTCw8MxeDDfwjU3NxdFRUUYOHAgZF1RPUNs3t2hd2Nj0ka4S9zV/YurGqogtZei\nv3d/k7fbpGwCx3FawddOZIdGZSMYGDhdUdBAlQ2V+M/Z/+B8yXlwHAdHsSMeHPAgJoZM1NinkI1e\nr6hHfa0vOHu+n63Mg++7JHWQ4lLpJYzrNQ595H0gc5ChurEaro6uAPgsv6imCLP7zdbYvzkHGGmr\nor4C7//2PlwdXdVjkTcqGrE5fTN6ufdCH3kHz2ebm1saGAFAsxP//4AAzYkkusOBA9rVt8Jz38JC\nfhpRlJQAACAASURBVGKItoRsWejw7etrfNYsZMvCrFYyWUvWbInn7MSmmRSYjx8/jnvvvRf1f8yy\n/uKLL+Kdd96Bn58ffv/9d4wbNw5KpdKsBSU9Q4xfDOKi4vDj1R/B/hh20MXRBS+MeUHnHMyGCnQJ\nhKPYEfXN9RqZ983bNzvd2Isxhn+f+TdyKnPQy60XOI5Dg6IB/0n7D+ROco2uV0I2+tO1y9hxcYdW\nNXWjohEeTh5/VAPbo7LhdVwqvQQVU4EDBwYGfz8O924ZovG+rmzQdbb4LJqVzepn3wDgaOcIB5ED\nEm8kagVmoQo7MVGYCCP4jx8+HoVFA/h7F078oE9TE5+pL1+uPXwlx+m+i6mu5sfE5jigqKhl+e3b\nxmXNQrbceg7o7s6ab90CtmzhZ6GiqmqbZlJgfvPNN7FlyxZMmzYN+fn5WLt2LVasWIF169ZhzJgx\n6gsuuTMpVAqkFaUhuTAZdmI7jAkag/7e/flnyRyHuVFzMbH3RGRXZsNR7Ih+Xv1MnqBC4GjniEcH\nPYrPUz+H1F4Kqb0UlQ2VcBA7YH7/+epyVTVUwdnB2aibgOzKbGRVZKmDMgBI7CRwl7jjp2s/6ewT\n/fPHE3EizQ0OYgfYi+3V++dkJXhjyxDsVXd/ckdk2FAU1hSiobkBHlIP1N30gdTe+BsJU/tQ1zTW\n6LxxcbRzRGWD9swWQhV2WlrnJsLAzZt8dau5MsozZ/guUiEhwIIFhr2HMWD6dD5jbksuN3wbO3fy\nNwW3b7csF4m6N2s+cAA4cgQYOhSIje36/ZEuY1JgHjt2LObP5y92/fv3x3//+198+eWX+OqrrzBj\nxgyzFpDYFoVKgY+SP0JyYTJcHFygYiqcyD2BqX2m4rFBj6kDm6fUE57STgzKr8P4XuPh4+yDw9cP\no+R2CYYHDMfkPpPhLfXG0eyj2JmxE7ebb8NOZIcpfaYgLjJOHTTbU9lQqbOaXOYgQ3GtjkgIoKbc\nGZOH9kVKYQqUKv6i78iJ4KcYjQAXzWd/EjuJRlaaU2bskfNM6UMNAOGe4VCoFBoDsQB8wI726aLM\n99YtvnHSM8+Y5xl0UxMfBMPC+L67995r2DNWNzfgoYc6t++GBv65dNspGZ2d+cy1qanrM9hbt/ju\nXuHh/OcwZkzLPo8dA0aM6JrhS0mXMCkwu7ryz8OuX7+OPn+0dFy6dCn27duHffv2ma90xOakF6cj\npTAFfdz7qC/yKqbC4ezD6ueqnZFXlYf9mfuRWZ4Jf5k/7gm/B5Feker1EZ4RiPCMUO+3qqEKR7KP\n4Ku0rxDgEgBPqSealc348eqPaFA0YMHgjjMrP5kfVEylFbgq6isQ5a0/qPjJ/HBP2D0ory8HYwwe\nTh4oyOv4RsBYzcrmPxqLmVZd38+zH6J9o3Gu5Bx8nH0g4kQovV2KANcAjAoaZd7CCg4c4KuOzdVy\n+8yZlgmgq6v5PryGZs2d5eQEvPxy57bR1MQ/654+3bR+zQcO8J+hqyt/J5aUxGfNOTnAxx/z47HO\nnNm5MpJuY1JgHjduHF599VWsX78ep06dwujRowEAM2fOxLFjx6jh1x0suSAZMgeZRgATcSLYcXY4\nX3K+U4E5szwT6xLXQcyJ4SZxw7Xyazh74iz+MvwvGBs8VuO1qYWp+PbCtyivK0dKUQoCXALULbbt\nxfbo7dYbR3OOYk6/OXCTuLW7X3+ZP0YGjsSZ/DMIdA2EvcgeFQ0VaFQ24k8Rf2r3vXYiO/g4+5h8\nzO25eusqvrvwHbIqsuBs74wbVa8gWBVodItusUiM4h+eR1FWCc7WFkPFVPBx9kGjiz9eP+Sgtxpc\nJtOuuq6tbadBWnMzX91bUcFnd/37m6e/s5AtCxmyv79xWbM1SEoCvvySb5w2SM9c0/oI2bLQAM/H\npyVrTkjgbxz27uUDtbOz2YtOzM+kwDxq1ChER0fj4YcfxqA2J9HEiRORxrcIIXcgO7EdVEyltZwx\npjHLk7EYY9h2YRskdhJ4Sb0AAFJ7KZwdnPG/c//D8IDh6ufUGaUZ2HRmk3p4y5SiFBTXFiO1KBWj\nAkeB4ziIRWKIOBHK68s7DMwcx2HpkKXwkfrgcPZhNCoaEeIegr8O/2unawBMdb3iOtYmroXMXobe\nbr3RpGxCbmUunItLMSxgWIfvb/s8OjHRDjJZINxkgRrjbLdXDa5vPG6dgfzmTeDZZ/kRrzw8+OzO\nzo7P8DqbNbfOlgE++ItEfNbs7Kx7DFJr0tQE7NrFP9P+/nt+1DBjsmYhWxZG+JJK+c971y6+IUDv\n3sCNG/zNCmXNNqHDK+X169exe/duLFq0CPJWjSGkUqlWUBb0MWT4O9IjjQkag+O5x6FUKdWZW7Oy\nGQwMMX4xBm1DoVLgTP4ZnMw7CRVTYWzwWMT4xSCrPEtrqkapvRRldWUoqS1BsBv/jG/vlb1wdXSF\ni6MLGGOQOcjAGENxbTFqmmrg6ugKpUoJFVNpDXaij6OdI+YPmI+4qDg0q5rhKHY0S79oU+25sgdO\ndk7q5/SOdo6QOciQV52Lfl79NFpY69L2ebTQkMuQxlv6um4J63T64Qc+gObl8YGi7x9zKXt4dC5r\nZozPBpubNbtIKZV83+TmZiAri69qttZuS0lJQFUV/7nk5PBduwzNmqur+ck12nYRa24GPvuM71Mt\ntEinrNlmdBiYV69ejW+++QZFRUXYsGEDAD5Yv/POO1i0aBFGjeqiZ1DEJvX37o/pfafjYNZBiDk+\nMKugwoMDHvxjcob2KVVKfJz8MZILkuHu5A4OHD7//XNE+0RDLBJDoVJoNNhijAEMGq2sc6ty4S7h\nmwtzHIdIr0ikFKYAAOqa6+Bk54S86jxMDp3cYbbcllgkNqiquKN+xyYFt1YyyzPVxyhw8byN4mIv\nXGVN8Gg1Vos5+jq3ZnTXrZs3+apVFxd+BC6Vig8YQu8NFxfTs2aOA/78Z360rba++47vu3zxIh+c\nw8KMLLiZtDf9pJAtC63T3d2Ny5qdnfmRxdpOulFQwE8t6cv3S4ejI/8ZUdZsEzoMzIGBgThx4gR6\ntRrrtU+fPvjwww/x1ltvoba2FpMnT+7SQhLbwXEcHol+BGODx+LCzQuwE9lhkO8gg4IyAGSUZSCl\nMAWh8lB1RuoucceFmxcQ6RmJq+VX0dutt3pdUW0RIr0j4e3c8iwxyC0IhdWF6mwy2DUYCqUCyYXJ\nqKivgFKlxIywGZjbf66Zj75FR8Grs/2SfZx9cKvuFhycWrqZTV52CLlVuYiPjUeIu2E1Ad3ihx/4\nbM7Dgw/SOTl8H9/W8ymXlvIjdrXtf2wIXQE3P5+vvu3VCygr44OfJbLmhgZgwwZg6VLdg5sI2bJQ\nfeHublzWLBbrrmk4dIiv2i5r1cRfJOKfOVPWbPU6DMzu7u4QiUQICtIcw1gkEmHVqlV47rnnKDAT\nDRzHIVQeilC58dPXXSi5AAexg0Y1McdxcLZ3htxJjmifaJy/eV49ileQWxCeGPqExjZmR8zG+pPr\nIbGTwNnBWT3E5eKYxVg8ZDFcHF1MGsyEMYai2iKU15fDS+oFP5mZU1EjzAyfifdOvwepvRSOdo5g\njKGgpgBh8jD0duttsXJp+SNbXlH2EoorAgGmAuobgOuRfOMvdH6uap327uVH8hKJ+GzUUlnzqf9n\n77zj26qv/v+52tOSPGR524lXdkKmswMJgQBJSEsh/RUoo2W2ZVNanoeWh9WyIdDSPpQUaAsEeCAD\nErIHSTyyE+94b0uyZWuPe39/HCRblrztzPvmpRf2le7VVwrk3M/5nvM5B4EjR0i53nNP8HP+ojWf\nL9jcxO0e2l5zd5KTaf++JyJReP9snguKfgPzPffcgzlz5iAyMhJLly7FkiVLMHfuXMhk9Beb2+3u\n5wo8PANHLpbDx4WaPXhYD3QyHe6dcS+q2qvQbGuGVqZFRmRGSGp5gn4C7p95Pz45/QlqLbXgwGFe\n8jysnbgWSsnQlILdY8ffjvwNx5uOQ8gI4eN8mJ0wG3dOu7NPz+xw9GYEAgw8SF0RdwVun3o7NpzZ\nAC/rBcuxmKCfgLuvuPu87n2H8INabmLikCr74UMLnEBLB7AgCVCru1L6hw9TWnvCMKdq1dXRfrY/\ny8cwpBBHUzV7PKR04+KAwkJg1ixSy19+SWn7Q4eA668PVs0sCyxcSIG4J3J5V6p/KKwZvWwQz+jT\nb2C+++67kZOTA5vNhg8++ADPP/88JBIJpkyZAqlUyhd68YwoM+Jn4Kvir+D2uQNV1h6fB17Wi1mJ\nswasxuckzsHM+JkwO8yQi+X9FkP1x79O/gsnmk4E0ugcx+Fw3WFoZVqsnbR2UNfqzQgE6N8MxA/D\nMFg6ZinmJ89Hk7UJSrEyKJ3fHz33uH0+imdyefDxYe1PW61UGe1yUZDyddJxjusKZJN+MDBxOIAP\nP6QerBde6KowHgrffkvp4Z5+2QUF/U+Z8vkoUMoHOVDl0CFa//z51Lr06quk0q1Wsue0WIDNm4NV\ns0wG/PjcTj3juTjo97/+1NRUvPrqq4HfS0pKsGvXLmzfvh3l5eV49913R3WBPJcP1e3V2FK2BZ3u\nTpw+exrxEfHQyXRgGAY3T7g5qDWJ4zgcaTiCLWVb0GJrQXpUOlZmrsTYSKr2dXqdON54HJXtldAr\n9ZgRP2PQhV5+Ol2dOFR3CIkRiQE1yjAMEiMSsbtqN9aMWzNo1TxSyESywJjLwTCa3tsBlErgrbdo\n//iZFCC+mzIUCIItLw8coOBsswFHj5LiHCrz59PwDIUiVH1H9rP3/t13wIkTwJNPDlxZ+1PS7e3A\n+vV01/XVV8CpU9RTDNAdTjjVzMMThn4Dc89hFFlZWcjKysJ9992H4uJiPPvss3jpnPxfznMpU2Yq\nw0sHXoJYKEZ6ZDq0Mi1qO2oxPW46bpl4S4ga/O7sd/j45MeIUkRBK9OizFSG5/Y/h6fmP4UYRQz+\nfPDPaOxshEQogcfnwedFn+PxuY8Pqe/Y7rGDAROSMhcLxfCyXji9zqDA3F+q+rKBYSidnJwMRAOI\n7+V1Xi8FMr2+a57yFVcMXTUnJZFalcvJ/Wugdpg2G/D116RyS0qA7Oz+zwEoBW+x0M/19dQ3/eWX\nVMjlL8wSCGjPu6dq5uEJQ7+VBbfeeisefPBB2LqbswM4ffo0Tpw4AbZnmT4PzyDhOA6fnvkUCrEC\nBpUBMpEMiRGJmGaYhiJjUWAkoh+Hx4Evir5AkiYJWpkWYqEYeqUeKrEKn535DF8WfYkWawtStamI\nV8cjRZsCiUCC9wreC2t+0h9RiigoxArYPcEjDDtdnYiWR0MtDZ4t7U9Vh3v0FrAva1paSOHK5VSw\n1NxMqrk/etuD3b2bArzFQkFzoOzbR2l3rZZuDgayx+tvd1KrgYoKUumVlVTMVVND+wP+h9FIqtlk\nGviaeC5L+g3M06dPxyOPPIInnngCVd02nz788EOsXbsWRuMQHfd5eH7A7XOj3FweYvYhE8ng9DrR\nbGsOOt5kbYKX9YZMpIqUR6LUVIoDNQcQp44Lek4n16HV3oqGzoZBr08kEOEnE3+Cxs5GtDna4GW9\nMNlNMNqNuHnizcMaKXnZ4/FQANN3sy2NiqIWowMHej/Pbgeee46CenesVlKlBgNVY3/5Zfjiqp74\n1XJsLL1/WRmp5v44fJhS2C0tFMg1GgrMOTmUsr7nHurP/vWvSTEvXDjwqVU8ly0DyhX5+5a788c/\n/hGzZ8/GYn68GM8wEQlEkIlk8LCeQOrZ6rZCLBCD5VjIRcGFOP4WqJ5DJVw+F5RiJZxeZ6CdCqBU\ndLm5HMXGYry4/0WsGbcGC1MWDmiylJ8FyQugFquxsXQjGjobkKxJxn0z78P4mPGD/rzDNRe5GOn1\nMze0wuCrDw6wbjeQnw+8/TbtNUvCjATdtw/Iy6O09c9/3nV89+6uaU5SKe1vHzoELFrU9wL9avmH\nbpOA6cnvf9/7XrN/b1kspsIyoRDo7KSbhooK2s8+dQq48056nUhEa775Zr6PmKdPhlz6KJfL8aMf\n/Wgk18JzmSIUCHFV2lXYVLIJPs6HYmMxWLCwuW3IjMoMUcYxihhMiJmAElMJEtQJYBgGLMeivqMe\nP5nwE9R11OFo41EkRCTA4XFgX/U+WN1WKMVKSEVSrD++HqWmUtw7495BtRZNjZuKqXEDsxXti8ux\nJOOlu8spl99z37jUClSpAXSrbN+/n9K+Ph8p0oULg8+x20ndZmdTQF2xghR3d7Xsx6+ac3LCB3gg\nWC376a6ae9trbm/vKiZLSgqe6azV0s2ATkev+/ZbstxsaKCbh+v7Hn7Cc3kzjJ4EHp6RY2XWShyu\nO4yvir+CUqyEQCBAnCoOOpkO7+S/g6fmPxUIoj7OhyVpS1DbUYuzbWchEUrAciwWpy7GNenXoN3Z\njjJzGarbq9FkbYLJboJaqsbsxNnkoS1RI7c+F9dmXDukiubLArebAop+BCZjtbTQ3cjddwM/TKIL\nkJlJDz8WC5lrxMfTnvMXX9A53YPqvn3UfhUbS5OqvvmGVPOBA/ReHk/we1gswOuvkxXlxImh6yss\npOv1TIuzLKnt3gKzXg8880z/n/+LLyjNLRbTTcPmzcCSJbxq5ukVPjDzjDpWtxWFrYVw+9xIj0wP\n65glFUmhECuwOHUxhAIhZCIZdDLaiys1laK2oxbJmmSUm8uxLm8dOlwdAEczlxelLMK1GdcGxivG\nKGPw7JJnkVefh9cOvYbxMeORGZUZMBfx9yHXWGrCBmYf6+t3UIXb56ZxlmEmZl0SqeqdO+nxwgu9\nK82BsnUrqdnPPwdmzOi72nrHDkopnz5NX1ZsbLBq9qtlv7qNi+tSzRMnAk89FXpNiwX43/+lIP78\n85Ry7s706ZQ2D4ds8A5xQfjVsv8P3u9Zzatmnj7gAzPPqHKy6STW5a8LTJgCgBUZK3DT+JtCgl6r\nvRVx6riQYCdkhLA4LbDKrXjt0GuQCqWBKVNOrxO7q3ZjYcpCoJsAUUlUuDLtSpxqOYVSY2mI45ff\n5rM7Hp8Hm8s247vy7+D0OpEQkYCfjP8JJhsmB/az6zrq8OmZT3G6+TSEAiHmJ8/Hj8b9KKgy+6JP\nVftTux0d5KC1YMHQr9XSQkEoI4MMPwoKQlWzH4uFgpjTSQqztpb2d7ur5u5qGega8ehXzT2sgwEA\n//43GZeUldFghwceCH5eIKA95dFg5076Pp1OegCklL/+mlfNPL3CB2aeUaPD1YF38t+BRqoJBEYf\n68Pmks3IisrCFMOUoNdnRGWgwlwR1LPMcixYjkWsMhb59fmwuW3Q67rSqzKRDBKhBHur94Z1A7sq\n7SocaTgSaKsCgHZnO9QSNSbog80nPjr5EfZU7UG8Kh5e1ovvzn6HD098iHh1PM12ViegobMBceo4\nJGmSwHIs9lXtQ3V7NX6/8PfDmjd9QeEvhIqPp6A4e/bQVfPWrRQ8hULat+1LNe/bR61SpaWkLE2m\nLg/pI0doHdu20V5uzxGPBw8CN95IVdHdMZlIhcfFAUVFwF/+AvzkJ7T33Bd2O/DRR9QHPVgXsJ7X\nCZcKF4tJTfOBmScMl8jfJDwXImdazlCldDe1KhQIoZaqsbd6b0hgXpW1Cs/vex5mhxk6mQ4unwv1\nHfVIjEjEnw7+CSebTqLR2ggBI0CyJjmguOUiOYz28G17E2Im4JaJt+CLwi/A/fCPRqrBwzkPBw2y\nMNqN2F+9H6naVNR31CO/IR8CCGBz21DdXg27xw6zw4xmWzNEQhHi1HG0Dm0yKtsrUdRahEmxk0bh\nWzzH2GzAxo2kSGUyyskPVTX71bJfxarVdL1wqrm8nFLRS5Z0jT88fJjcwJKTafgEw9CIw3DtT0Jh\neNW7bRudZ7HQZ3O5aE7x73/f99r37QO2bCH7zqVLSXXPm9e7l2pv3Hpr1882GwX5oQ6m4Lls4P8L\n4Rk1nF5n2ONioRhWtzXkeHpkOp6c9yRiFDGotlSj092JcTHjUGOpAQMGmVGZ4MDhSOMRVLZVBs7r\ncHVgQkz4wQcMw2BFxgq8tvw1/Gb2b/DE3Cfw8rKXQxzAmq3NgX7kM61noJKoYPVYAxXhaok6MOe5\noq0iyGyEATPo/miO42Bz23r9js4b/lSxf281OppU81CG1WzdSq5ebjcFWIeDgtKrrwZPOGpvB/70\nJ3LrKi6mAG21UvpZryezEaeTAmxiIgXLno+UlNCA51fLBgMVeEmlVCX96afURtUb/n3stDRyJCsu\nppuVDRuGPljC56Pe7H37hnY+z2UFH5h5Rg2/b3VPt602RxtmxM0Ie052TDb+a9F/4e83/B1vLH8D\njZ2NSIxIhEqiQpQiCnGqOLAsi9Otp+HwOFBrqUWkPBLzkuf1uRaNTIMphikYFzMubP+yVqaFj/PB\n6XHC5XVBIpTA7rFDwAggEUogFoohFAjh8rnAgEGnqzNwLgcuMPt5IFS1V+GF/S/ggW8ewP1b7sd7\nBe+h3dk+4PNDyMvr2r8cDv695ZgYCiQ+Hym8tjZSzYPFZKIUMsd1PRobKaVcXNz1uu3b6b3ffZdU\ndkMDBVKGoYfJRB7Ug2XXLgr6hYVkYuJ206OjA3j//d7P89+c6HS0rjffpJ9Pn6bRkUPh+HFK0X/5\nJal2Hp4+4FPZPKNGUkQSlqQuwY6KHdDJdRAJRDDZTUjSJPUbSMVCMcwOM2weWyDoCRgB5iTOQbm5\nHKeaT6HF1oIlqUtwXeZ1IbadgyVeHY+J+ok41XwKDMPAx/ogZIRweB2IVcWC5VioJWowYOD2uQMz\nkFvtrYiUR2KiPkwbThhabC146cBLEDJCpGhSwHIs8urzUNtRi2cWPTMo0xMAZPX41lvA7bcDy5YN\n4ZMTv/0t0FTiBQpvpzahHzAoOvDSrCYKpoNNZz/8cPDvLS00HCI+ntLUEydS4Ny6ldTpsWPUOuVv\n0/I7ZCUkUGCrraV+4YEyYwa9/oMPgKlTKT0O0A3HqVOA2Rw61KJn1bdUCuzZQ2MU29tpjOVjj9EN\ng9tNa549u+91+Hyktg0Gusk5dAjgjZl4+oAPzDyjBsMwuHXKrRgXPQ57q/fC7rVj2ZhlWJCyIGjf\nubf2JKVYCbFAHDQCUiQQYaxuLNQSNd669i3IxcMozOmx1num34N/nvgnqixVqO+oh0KsgEgggkKk\ngMVpQXpkOgSMAA2dDehwdcDitCBFm4JfTv9l0H51X+yt2guPzwODhtpnhIwQSZokVLVXobC1MGTf\nvV82bqQU7ldf0VSlIRYqNTUBqVM0wJScoONVVQD+fu2QrhnCN9/QXnBcHPUcX3cd/dvf4ztpEqlj\nmw0YOza4QMxsBjZtAu6/f+Dvl5ZGwdNopP3n7unzzk6aJHXLLcHndK/65jj6AvwV4mPGdKnm9HQK\nsH/7G7Vg9bX3fPw4ZQrS0ugz+Q1PBjpcg+eygw/MPKOKgBFgVuIszEoMHePn9rmxuXQzvjv7HVxe\nF5I1ybh5ws0YryebS6lIimVjlmFj6UakaFIgFAjhY32o7ajFivQVIxaU/ailajw460H8ePyP8dGJ\nj3Cy5SSaOptQbalGjCIGEZIIZEVn4a/X/xUurwtioRixythBuYdVtFWEDL0AaJ+6ydqEKRhEYK6r\nozR2aiqlag8cGJZqHlVaWoC9e2mPWCikKu8NG2jEYtwPvuZyOSnWceNCxzUCVNU9WHS60PYoPz2b\nyrdupSIvrxeorqZ97ooKCqAlJfQ9K5Wkmn/1K9p7Fwrppuihh8K/h18t+5W5UtllE8qrZp5e4AMz\nz6hitBuxrXwbjjUdg0qiwrIxy5CTlAMBI8A/jv0DB2sPIjEiEWKBGO3Odrx88GX8fuHvkR6ZDgBY\nlb0KLp8Lu6p2ARzt5y4dsxQ/Gj/ydrA+1oeChgJ8Xvg5ykxlUIgVWDpmKZaPXQ65WA6VRIV4dfyg\nAnFPEiMSUW4uh1amDTrOgUO0InpwF9u4kQKcQEAKb5iqebj0Nu7SYABeyv6mq20KoPV++SUFrKio\nLreuqCi6yBNPDN/cA6CU+JVX9v86k4nao6KjgZ/+lFLVn39OaluppO+4pob+feQIfdcdHaSCjx8n\nZR1ONXdXy36624TyqpknDHxg5hk1THYTnt3zLFrtrVCIFbC6rPhrwV9R0VaBZWOX4XDdYaRp0wKB\nTifXwcN6sKlkEx7Oof1JsVCM/zf5/+GGrBtgdpgRKY8c9n5yOOweO145+Ao+L/wcdo8dCpECEqEE\nXtYLs9OMZxY9A4VYMez3WZy6GLurdsPitEAj04DjOGxclwN3x3V448upEHYrxzQY+jAr8avlZDJa\ngVxOPcDnUTX7x10GcLmAkydRZR0H1HxHa2zoVr1usdCx7k5cKhUFK7OZ9qLPFdu20Y1AezvdHKSn\nk9q+6abQ17rdFMRjYiiAy2S9q+ZDhygVXl0dfFwgIMOTcBahfhobaQ0jcYPCc1HBB2aeUWNT6SYc\nrj8Mt88dsMFM0aRge8V2xKvjIWAEIepTJ9PhbFto5WuENCIoINvcNuTV56HEVAK9Uo+5SXPDWn0O\nlG/LvkVuXS7AkaoFaO5zXWcdZGIZ8uvzsSi1nwlFAyAhIgGPzHkE64+vR42lBhw4COw/xoqZY6CU\nBFtFfvFF7/ObDQ0teCnSSYHEj1hMSuw8quYgqqqoPzkhFrh/TWir0bJlpGj7m/w02vjbquLj6YbA\nX+CVkRH+9Xv30j60PxUeG9u7ar7nHuCOO8JfR9HHjZ7bDbzyCv1Z3njjYD8Rz0UOH5h5Ro1/nfwX\nHF4HImWRgcBc2V6JGEUMOl2dYUc3Wt3WfgOs2WHGiwdehNFmhEJMhVkfnvgQt02+DTdk3TD4ymYA\nu6p2QcgIg86Vi+WwOC1gORYlppIRCcwAMF4/Hi8tewmttlaIBCI8+n9RUIYx1nI4eq8pqjqrCW9t\nKZHQiYMMzL35ew/Z29vlovYgnY5U8vXXX7jKz29CIhKRCvYXeNXUAFdc0VXNDdDn+vxzeq3Z5Mf9\nmwAAIABJREFU3HXc7Q6vmsViegyW3Fy6K/vmG+Cqq4CIkc8S8Vy48IGZZ1QwO8wwO82QC+WBwMsw\nDCKkEajvrEeyJhmZUZk4az6LxIhEMAwDp9cJs8OMn0/9eZ/X/qr4K5jtZiRrklFsLEapqRQe1oOn\ndz2N3VW78UjOI2HtOfvC7XVDKVHCx/mCn2BIOcco+rFwHCQCRoBYVWz/L+yNsWN7LzgaAiPu711V\nRYVP/mroQ4fI1etc09FBaWOVKvzz3dUyQAFaqaQWq/JyUqvdK7edTipO62m4MmZMcAAfDv45z3Fx\nFPx37uRV82UGH5h5QrC5bdhXvQ+H6w9DIpBgUeoizEmcMygvaKvbigR1AqraqyDjZAFXLbfXDQEE\nSI9Kx4NRD+L9o+/jZPNJMAwDiVCCO6fdiamG3mcecxyHQ7WHEKeOQ6O1EUXGImhlWggYASxOCzrd\nnXj98Ot4ednLkIoGXlgzO3E29lbvhZARwuV1QSqS0uANloNaosbcpLkDvtZlj18t+4OhTAZ88QkV\nO51L1cxxNFVKJuu9zeq774KVr/+8Q4fo5mfHDmD58q6eao1mcC1bQyE3l7YoUlMpA8Kr5ssOPjBf\npvjHHlrdVsSr46GT0188Do8Df/7+z6iyVCFaHg0f58N7Be/hVPMp3Dvj3gFXJOuVesSp4iCAAFWW\nqsBxBgyuHHMlmXUwDB7OeRgmuwk2jw2xytiQYNrp6sTeqr3Ib8yHUqzE4pTFXWnxtkrIRF1BHwA0\nUg06XB0oMhb1GeB7siprFU63nEaqNhVn287C7DSDZVlMMUzBIzmPDE/dXkL0VXkdSIfXNAMWHeCj\nwGzQWqn16Fyr5spKascCgJUruzy7OY7Wo1bTgIl77gk+r7WV+paTk6lAbdu20H7n0cKvlqN/qNCX\nSCjzwKvmywo+MF+GmB1mrMtbh8q2ykCQuybjGtw0/ibk1ueiqr0qKBWskWqQW5+LZWOXBdqY+kMm\nkmHNuDX4+OTHyEnMgYf1wO6xQ8gIcc/0e4ICfJQiClEI7VHtdHXihQMvoKGzAdHyaLQ52rAubx2U\nEiUaOhvg8rkCKt7pdUImkkEj08DisgR5WQ+EGGUM/rj4j9hfsx+nW05DAOq/npc0b0h71oOht/3d\nC6F+qychldc/UHXWh/V37KV970deAOKtwROpnD/swS5Y0Pc85pGC4+j9FAoKbBs3dindoiKaM/3U\nU8C0aaHnvvsupaYjIugPoadqHk1ycym9npTUlS6PiqKBGrxqvmzgA/NlBsdxeCfvHdR11AUmNPlH\nMcYqY3G08Sg0suDReQzDgAGDUmPpgAMzAFw99mpoZBpsKtmEZlszJuon4sbsGzEuZtyAzt9bvReN\nnY1I03bdJGhlWpSby6FX6sGAgcluglwsh0ggQk5il2tViiZlwOv0o5FpcH3m9bg+89wOsO9tf/e3\nvw0fsIFhFGWNFs3NwHvv0cLuuYeCYU/E4nM3WcmvllNTKUjn5pJqTkgAPvmExkQ+/zzw8cfBa+rZ\nhiYW0/nnSjVXVtJedWdn8HGVCqiv5wPzZQIfmC8zajtqUdFWETQ2USgQIlYVi2/KvkFGVAbcvtBJ\nQhw4KCSD6+NlGAZzEudgTiJVD3e4OlBmKsPRxqPIisoKsuUMx9HGo4iUB3sZ+4dKrMxcieszrsdr\nh18Dy7HIisqCgBGgsq0SV6ZdiYSIhEGt9XTzaWws3Yjq9mpEKaKQGJEIqVCKpIgkzE6cHXKzMpK0\n2Fqwo2IHio3F0Cv1uHrs1ciMyhz5gqwR4MgR6gwKgmXhq+eAVUpSpk8+SUVU54vuatk/CEMqpbUt\nXkwpdaGQxk8WFACzurnSbdpEae7m5uBrfvPNuVHNt91GD57LGj4wX2ZY3daw/cNykRxN1iYsSF6A\nA9UH4GW9gTSx3WOHWCDGlNhB+jh3Y2/VXnx08iP4WFJSYqEYd19xN2YlhFp1+lGIFWi1hR/Pp5Kq\nMCthFqbFTcN3Z79DQWMBVGIVbhx3I+Yl9T0gw4/b50ZuXS42nNmAg3UHMUY3BnqlHltKt8DqtmJy\n7GTo5DpsLN2IJ+c9iSTNIAYoDJC6jjo8v+95eFgPtDItzrScQX59Pn4x/ReYnzx/4BdyOin4jHJA\ndDi6tmoDmNtR5xZR+rWoiIwzMjNHdR19UllJdw/JyV3KXa+nwRmlpdQGZbGQCn3jjWDVnJ5OBV49\nEQrDj3w8fZqqp4diF8rD0wt8YL7MSFAngAMXFHgBwOQwYXzMeGRFZeHH43+ML4u+DDwnFopx38z7\nAgVig6XGUoMPjn+AOFVcoLjL4XHgvSPvIVWbCr1SH/a8JalL8MbhN6CVaSEUkPmG1W2FTCQLzF+O\nUkRh7aS1WDtp7aDW5PF58ObhN3Gi+QQKWwvBciyKWotwsvkkVGIVDCoDGjobMCl2Ekx2E9YfX4+n\nFz49LDvOcHxR+AU4cAFTE5VEBafXiY9PfoyZ8TMHVlnu8QDPPUcuVVOGfvM0JFiWbCuFkXRToFKR\nycn5VM1lZXST0lP1OhzA/v0UlP03Mj1V82Bc02w2muyVnk7TvWL5AkGekYEPzJcZGpkGK9JX4OuS\nr6FX6iEXy2Gym+D2ubE6ezUYhsENWTcgJykHZaYyiAQijIsZB5Wklz7QXvCyXlS1V8HH+pDfkA8R\nIwoKMnKxHJydQ0FDAVZkrAh7jSvirsCKzBXYVr6t6zyRHL+a9as+0+Asx8LsMEMqlIYdGAEAxxqP\n4VTLKSSoE3Cm5Qy0Mi08Pg+KjEVQ69QQC8Wwe+xweByIVkSjoq0C7c52iAQilJhKAACZUZnDsgdl\nORbHm48jQZ2AJmsT6ix1AIBETSJcPhfqOuoCM637JD+f5ht/9hlNaBrFfVy5PNhsDJ02wCmDXPKD\nMo2OPjeq2ecLtvLszvLl9OgOxwHPPAOcPEkB1eulgRlicahqHih79lCA//hjCvbPPXd+U/g8lwx8\nYL6AaOhswOG6wzDbzRgXMw7T46cPeJzgYFgzfg30Kj2+LfsWrbZWjIsZh1VZq4IqsaMV0YMfqvAD\n5eZyvJv/Ltqd7YHfYxQxSNQE50CFjBBmhxmHaw/jSOMRKMQKzE2ai8yoTCo4YxisnbgWi1MWo6Kt\nAlKRFOOix/UZlE81n8JHJz6C0WEEQMH91sm3huwRH208CpVEBbFQDAEjAMuxATVsdVvh9rlhtBtx\nsvlkoPgsrz4PGwo3BNLxQoEQt0+9HQuSBzmnGJTCzqvLQ3VbNUqNpeh0dQZuXGosNYG19YvHQ05U\nKSlUuHTq1Kiq5unTu1Vle73A1r2A2osqR2yXQnW5Rlc1NzRQ5fRTT5EZyEBoagKOHqU1Op0U1G02\nCthHjwKFhX37VvfEZqP9aIGAlPju3WRI0puNJw/PILhoA/PWrVvx0EMPwefz4e6778aTTz55vpc0\nLAoaCvBu/rtgwEAqkmJ/zX4kn03GE3Of6FX1DRUBI8DClIVYmLJwRK8LUIHXq4dehUwoQ7KGKlvd\nXjcO1x9GsiY5ECA5joPT60R+fT6+O/sd1BI1vKwXe6r24MZxN2J19urANePUcYhTxwV+5zgOha2F\n2H52O0xOEybETMDSMUvR6erEa4deg06uQ7ImGSzH4ljjMZjsJvzXov8K6neWiWWBdH6KNgVnzWeh\nlWkhFUrRbGsGBw5aqRZmhxmV7ZXIjs7Gf079Bwa1IXCz5PK68I9j/0CaNi2Qih4I+6r34YNjH0DA\nCODwOnC65TSiFdHQyskoheM4WD1WdDg7gP5qzvLzySAjNZVU5Gef4bf/noSm5lD1F24oxm8fdqGp\n3hfi29znAA0/LEv7uCwLGFXA7Nldz6lH9r/ZIDZvJuW7Zw/NdB4IcXGUbi4t7bIstdtpb/jmm4ON\nTziOKrNnzAhW5Q0N9HtsbJdaPnuWCsLMZmD9+mDVvG0bpbnHDiDrMVCGYLfKc/FxUQZmn8+HBx98\nEDt27EBCQgJmzpyJlStXYty4gbXhXGg4PA78/cjfEaOI6ZoxrASq2quw7ew2/Hj8j8/vAgfB8abj\ncHqciFV27bel6lJRaCxEQUMBZifOBsdxaLW3QilRwuQwYaxubECtelkvvir+CrMTZgcF4+7sqNiB\nj05+BLVEDblYju1nt+NAzQGkaFIgEUoC6WUBI0CSJglV7VUoNZUiOzo7cI25SXOxu3I3vKwX42PG\nw+VzodZSG1iDWqKm9PYPRVkmuwkKkSIogyEVScFwDHLrcpE4fmCBud3Zjn8e/ycMKgOkIimMdiMi\npBFod7VD0CGAWqqGQqxAti4bx5uPY2JsHyrOr5b9hUdaLVBZiaa2dqReERny8nCtV0251UhlqqlH\ntlsqt682ra7nJEDEZDqeDeDnA2+lGzINDdTqNG4cKdbFiwemmjmO0v0cRy1TQiEFOZalfuHuwbOo\niPaOH3usq8+ZZYG//Y32pX/1K3pvgNqatFrKHuza1aWazWbgX/+in59+emQyByUlwPvvA3/4Q98D\nMHguei7KwJyXl4f09HSk/pBTu+WWW/D1119ftIG5oq0CHtbTFZR/wKAyYH/1/osqMLc52oKUKQCI\nBCLMip8FFiwkQgkEjAA/m/Qz5DXkodXWGlRQ5S9IKzGWhA3MVrcVn575FIkRiZAIycBCJVGhvqMe\nu6t2B4rCemKym4J+z4jMwI3jbsRXxV+B4zhEy6OhlqjhifRAKVZCIBCgw9UBtUSNeHU8DtQegNVt\nDZzv9DpRbCxGiakEZ9vOwuq2YlX2qpA5yz0pMZbAx/kCaWuxUAy9Ug8OHBgwmJ04GzGKGDRaG/tP\nZXdXy350OuBYFTBN138waGoCWloARSeNGEzov8XsvLdwbd5MxiVyOa19oKrZ46HAplB0DZVQKmnD\n3GQi9axUUuDesIEC94YNtC0gEFCqu7KSzvv3v6ngzX+H4nDQ643GLtW8bRu9T3k5Bfrx44f3uTmO\nHMHKyoB9+4Brrhne9XguaC7KwFxfX4+kpK7WlcTEROTm5p7HFQ2P3ip9OY4LCXIXOmm6NPg4X8jU\nKJvHhlVZq3DjuBsDFdanW06D5diw1xEJRThrPostZVtQ2V6JxIhEXJdxHViOBcuxgaDsR6/Uo8RU\ngg5XR8geNAcuZL+cYRiszl6N2QmzUWwshoARYIJ+Aj4++THKzeUhr/cXh3EcVbR/X/M9rG4rRIwI\nY3RjsLd6L4pNxXhm0TODqgtIUCegzFwGmVAGhVgBg8oAL+uFx+fBjLgZfZ+8dy8pudra4ON2O9DW\nBkSGquYgtmwBmGwKSIWFlO49VwYgQ8Gvlv3mHwbDwFRzQwPZbIpEtI/cfdqT1UqK139+URFQUQGk\npVHgPXGCgvPnn1Mblc8HfP89PV9REdzXrFRSir2gANi+nQZjWCwUUMeNG55qLi2lG4vMTODrr4GF\nC3nVfAlzUQbmkW5ZOd+M0Y2BVCiFzW0LCirN1masyl51Hlc2eMbHjEdGZAbKTGWIU8eB5VgqaHPS\noIADtQdw04SbMC9pHuYnz8ex5mPQyXWBGxCX10V91hyD5/Y9F7DZrDBX4MX9L/b6fXhYDzKjMuFh\nPWh3tkMj1YDlWNR31iNdl46MqPBFOT33rxemLMTRxqPQyXQw2o2oaK+AzW2DVCjFjPgZqGyvhNvr\nhtlphogRQa/UIzEiEQJGgDOtZ/B54ee4IfOGXg1JsqKzIGJEgUEZWpkW2VHZKGgoQEZUBmosNWA5\nFquzV2OMbkzfX/avfw3O6UR9Zz06nB0wqAyIVEQCDygBXZg5kt1pagIOHADk0wCphwL5AFXzOaO5\nmW4+brqJgppfLftvHqRSKjTrSzVzHE2KMploLnK4vzv8+8t+tRwRQa/T6brUc2UlZSZaWyn4Pvxw\n+H5nhqHBGP4xkpGRw1fNfrWsUnVlCnjVfElzUQbmhIQE1HZTCbW1tUgMcT0A/vCHPwR+Xrx4MRYv\nXnwOVjd4ZCIZ7p1xL97OexsmuwkiIf3FPTZyLK4ee/X5Xt6gEAlEeDjnYWws2Yj/K/o/FBmLIGSE\nmBA9ATaPDQ6LA28efhMKkQLT46djQfICfF/zPUQCETiQgcPPp/wcm8s2QyvTBgKcTESKck/VHkTK\nI2GymxCloL1VjuPQZG3C2olrkaJJwcenPg4EuGhFNJQSJTYUbsC8pHn9FmlNNUzF0jFL8cHxD1DT\nXgOxUAwRI0KqLhUunwurs1bjg+MfQCaUYVzMOKRq6XhefR4aOxvRbGvG7srduC7zOtyYfWPITaRW\npsWtU27F+uPrIWAEEAvFkIqkuHnizZgVPwtioRiTYycHRmH2RbvQg3fO/BXl5nKqLAeLJSlLwEp+\n1r8627KFAoc/yCkUF55q/vprUp7Tp9M+ekEBpaS7ZwhYlgY8XHtt+HWXlJDaBEjRzugjC+FXy/6t\nAa2Wfn/nna4gXFhIoyRzc4F160K/Z7OZ9qa7j5GMiBieavarZf+6YmN51TwI9uzZgz179pzvZQwK\nhuPC2dlc2Hi9XmRlZWHnzp2Ij4/HrFmz8J///Cdoj9k/nOFiwmg3Iq8+D22ONmRFZWGKYcqgBiiw\nHIujDUexr2Yf3D43ZiXMwtykuaPSctUXTdYmvHLwFdR31ONw/WHY3DYIGSEMKgM4cPBxPixKWYR1\nK9aB5ViUm8tR1FoEqVCKaXHTIBfL8dDWh5AUkRQSnGottXhg5gP46ORHaHO2BY7PiJ+Be6bfA7FQ\nHJic9Xbe2zDajVCKlXD5XPCxPtw7417MTpzdc8lBGG1G3L/lfogEIkhEEsQqYyEXy1FjqcGK9BWQ\niWX4svBLJGuTwXEc9lTtgdVthY/1Ybx+PMboxqCqvQr3z7y/13GRtZZa5NXnodPdicmxkzFJP2lQ\nf9Ycx+Hlgy+j1FSKBHUCGIYBy7GobKuEc+sfoHSHVgIHKq2bmoAnngD0evz2wHVosv5QQW2zARMm\nANHRA6vK7kZfU6eGtC/d0AD87nekijMygEceIQXNhtn6kEiAmDDzsjmOhlU0NlL6WiwGXnwxfP8z\nxwHPPttVZe2nupoC4zXXUNDNz6c1ORzUEtazruWTT2gPuvs2AsdRkdgLL9A0q8HAcbTm+vrgz1hd\nTdXkvGoeNBdDbLgoFbNIJMK6deuwfPly+Hw+3HXXXRdt4Vd3ohXRvZpt9AfHcfjn8X9iV9UuaKXk\nlLX++HocrDmIx+c9PqjZxMOB4zi8V/AebG4bohRR8LE+agECtUfFqmLh8Diwp2oPfKwPQoEQmVGZ\nyIzqMqPwp7N9nA8ipus/UZZjwYFDelQ6/rTsTyhsLYTVbUViRCJSNCmBIM4wDA7VHYLJbkKqNjVw\nvsPjwD+O/QOTYyeHFNp1p8pSBaVEiRRt8CCMaEU08hvy8fi8x7GxZCM6XZ3wsl5YXBbIRXK44UZi\nRCJEAhFiFDH4tuzbXgNzkiZpWBafzbZmFBmLkBzR5XkuYASIV8fDeeNbeOOaN3pX3M3NpOhYFi8t\n/Db4uXntwKrBb5/0OnWqatCXIvxpa4OBerMrKwffduRXy6mppFSrqoBjx8KrZq+XsgUqVehxr5dS\nyNXVpLoVCrpBOHw4NDBPmULXCUd/e/7haGqidXs8ZCXqcFAqOzGRXMz4wHxJclEGZgC49tprce21\n157vZVwwVLVXYU/1HqRp0wL7tVqZFqXmUuTV52FByuBNMIZCk7UJVZYqJEckw+a20b65WAkw1Cqk\nV+rBMAxEAhFqO2qDAqcfqUiKBckLsLtqN/QKPeo662B2mOH2urFs7LJAO1Rf85YP1h6EQRU8gkku\nlqPV1oqKtgpM0AdXb59qPoVNpZtQ31EPsUAMi9MSck2PzwONTINoRTQemv0Q/nb0b6i2VMPmsUEm\nkmFO4hwoxJRaVIgVMDvMg/36BozVbYUAoZ7nMpEs0IfNoJfAPGXKubfuHAzdi7wYhgLh//0f8Oij\nA08F+/dl1equc3Q6ckebNi1UNYvFwC9/2fv1CgvJwMUf5C0WSn33dCAbNy40WA8HgwF4+eUun+71\n62nf/dFHqUea55LkAtlM4hkuJaYSCCAIqeLWSDXIb8g/Z+tw+9xgQK5dSokSSokSTp8TAFVHu31u\nODwOJGuS+0wn3TThJiRHJGNT6SbkN+Sjur0aFrcFxaZilBhL+l2H382rJxxCK90P1R7CywdfRpO1\nCVqZFi6fC2eMZ1BmKgu8huVYGO1GXJV2FQBgvH48Xrn6FTy94GlMiZ2Cq9KuQoyyK9VocpgGPN5y\nKMSp4iBgBPD4PEHHTQ4TMqMyL7pq/iB6Fnnp9aSaKyoGfo2SEirSEgiosK2tjVRuVRVVcptM/V4i\nQM+iMID2nFtaSIGPJgxD76XVUrX98eOU0t67l99fvoS5iP/v5emOVBg+Ve1lvQEVdy6IV8dDKVbC\n5raBYRhM1k+GXCSH3WMPBMvsmGykadP6TOUqxApEyCJwRdwVWJK6BItTF+Oq1Kugk+rw/rH3e22z\n8rMoZRGarE1Bwd/qtkIhVgT5T3tZLz45/QlilbGIlEdCLBQjVhWLeYnzUNhaiKr2KlS2VaLGUoNF\nqYuCUtMigQgzEmZgTfYa1FhqAvvMzdZm+FgfVmatHMY32TdKiRKrs1ejxlIDi9MCL+tFi60FNo8N\nN42/adTed9RpaiKjDqeTgmhVFaWQOzpobONAcTqBOXOorSk1tesxcybtAX/66cCvVVVFqXS7nQrP\n/A+vlyqwzxXd0/sFBfS98FySXLSpbJ5gJsdOhoARwOl1Boq9fKwPNo9tcOMDh4lYKMZtU27DO/nv\noMPVgWRtMqot1ZAIJZgWNw1KsRIigQi/nP7LoOlWPXF6nTjTegYJEQk41XIKjZ2NYMBAJVFBr9Sj\n2drcqzMYACxPX47C1kKUmkshEUjgZb0QC8X4zezfBPVAmx1mWN3WkJuEZG0yvJwXt025DSKBCKna\n1LDFaABw88SbEaeOw7dl36LJ2oQJ+gm4MfvGgCXpaLEiYwUi5ZH4puwbtNpbkRGVgdVZqwc2+OJC\nRakEHngg/HODsfmcOpUePdm3j9R3Xh6wcmWYGZZhSE4Gnn8+/NjHc6Vau6f3BQLa8964kVzIeC45\n+MB8iRCliMIvpv8Cfz/6d/jYLoOPGzJv6NUNa7SYmTAT/634b+ys3ImmziY8OvdRaKVatNpboVfq\nkZOU0+uoRz8MqHLyUO0h2Dw2aKTUruLwOnCq5RSarE19BmaFWIEn5j2BM61nUGoqRaQ8ElfEXYFI\neWTI6wAECtH8eFkvpELpgKrahQIhlqQtwZK0JX2+bqRhGAY5STnIScoZ0Ovbne3wsT5EyiMvXC8A\ntRpYMEr1EG43VVLHxtIe8caNwP3393+eUHj++7vDpff9qjklpe9zeS46+MB8CTEncQ6yorJQ2FoI\nj8+DzOhMxKvjz8taxujG9G+Q0QdSkRRJEUn4vuZ7JER0/aXIcRxUEhVONp/EtLhpgWNFxiJsr9iO\nVltrYKhFjDIGUw1T+ywSU0lUyEnKwd7Kveh0d6LYVAyWYxEhjcDtU24/561mo0GztRnrj6+ncZUc\nEB8Rjzum3oGxujGUIg5nlDFIgj20g49fMBw+TBacqamkOAejmgeDz0fFatddN/yBExzXld6XSIK/\nZIuF9ssffHB478FzwXFR9jEPhIuhV42nbzaVbMLvdv0OQkYY2J+WCqWYYpiCxIhE/Pei/wYA7KzY\niX+e+GdgqEW7ox1SsRRPL3i6T1Xtx2gzYvm/lqPEWAKxQAwwgIgRYUb8DHyw+oOQ6u6LCYfHgad3\nPw2by4ZYFQ0WaXO2weV14cX42xD50Qbgf/5ndKdBXQi43dS7LRZ32W82NFC6eyCquSccR61L4VLZ\nBQWU+r7/fmDZsuGte+tW6mFOTQ2fStfpyICFZ8BcDLGBV8w8FyxpujRMjZ0KmUgW8MCOU8WhxdaC\nFA2l7+weOz45/QkS1AmBXm2VRIWGzgZ8VfwV7pt5X7/vc7D2IDqcHRgfMx4sx0IsEEMkEOFs21n8\n+9S/8UjOI6P6OUeTE00nYLKZkKpLDRyLlEeitr0GjevfRmS9m5yzVq/u/SKXAocPk9GI378aIDX7\n/fdDU835+eS+9cc/koOanx9Gb8JgAL76Cpg/f+iq2Waja7jd1FsePbT56DwXH3xVNs+I4fF5UN9R\nHzLJaahkR2cHTD6yorOQFJEEm8cGDhyuGkNtSzWWmqBpTX70Sj2ONh4d0Pt8V/EdJEIJFGIFVBIV\npCIphAIhZCIZ9lXvG5HPcr5osjVBJKTAoWtog8jtBQCMaXaTy1V2NvDNN+RMdSnjdJI61uu7HgYD\nMGnS4D+710vtU2VlFKC7c+wYtVHFxlIV94EDoeeXlVEA7489e8gLXCAg5cxz2cArZp4RIbcuFx+f\n/JgCJ8dhgn4C7pp2F3RyXf8n94JIIMKjOY/io5Mf4VjTMTAcA4PagHtn3BvwvJYKpQGP7e54fJ6Q\nKVO9IRVKwSK0/crH+sgc5SImXhUPL+uFxO7CvE++R3FOJsrnZGD83kLIIuNo39LrHRnVXF9PxVX3\n3x/e9vJ8cvXV9BgJjh6l4JuURFOnZs4k1exXy35LT72+SzXLZNST3NhIHqX33de3b7fNRvvHBgN9\nl7t2kcsXr5ovC3jFzDNsSowleDf/XcjFciRrkpGsSUaJsQSvH34dPnYAyqAPdHIdfj3713jrmrfw\nyvJX8PyVzyM7ustvOEWbglhlLIx2Y+AYx3Fo7GzE0rSlA3qPVdmrIGSEsLltgWNunxsunwtrxq8Z\n0ro9Pg8aOxvDOoidS6YYpkCv1EP9fQHENheyDpZAdrIIsc1WxCX/MO3IYBgZ1fz11xRATpwY/sLP\nBW43PQaDXy1HRdG+vMnUpZr9atlfTCeXk2revx/461/J9GTLFgq6n33Wt2r2q2WptGvYCK+aLxt4\nxcwzbLae3QqlRBloPWIYBgkRCahur0a5uRxZ0VnDfg+1NHxxkoAR4FezfoXXDr2GqvbFhzK4AAAg\nAElEQVQqgAPAUMvW1ekDU0hzk+Zi7cS1+Nepf6Hd2R6ws1yTvQYrMwdvErK/Zj8+Pf0pHB4HWI7F\nzISZuG3KbVBJVP2fPMJIRVI8Ofl+NLzzU1RrAU1bB67dXIy0sTlQ+DMKI6Ga6+qoyjk5mVTklCkX\nnmruyUcf0b/vumtgr/d4uoJvWhodi4qizzt9OgVbu508rf24XMA//kHnVlbSuRkZZFDSm293d7Xs\nx2DgVfNlBB+YeYZNQ0dD2KDDgAmaADVaJEQk4MWlL6KotQhWtxUJEQlBQy36QyQQ4akFT2Fl9krs\nqtgFDhwWpSzCVMPUQff7nmg6gb8d+RviVfGIVkSD5VgU1BfA6rbi8bmPj2r/sM1tg9lhhlamDbqR\nico9iShdFrIS48CZTJDs2Q8m1hfsHMVxpPyGGpg3bqQAr9NRADpxArjiiuF9oNHEP48aoLam/vq6\nGhuB11+nveqoqK7jajW1MOXnU9B0uYLP4zgK3BIJvV98PN2w9OXbnZ9PBWpeb/Bx/571pV6ox8MH\nZp7hkx6ZjvyG/CDrT47jwIJFrDL2nKxBIpRgimHogxkEjACT9JMwST9pWOvYXLYZOpkuML1KwAiQ\npElCYWsh6jrqhjVRqjd8rA9fFn2JbWe3URsIAyxJXYKbJ9wMsd1J6VODgRzP9HGk0tasoRnG3Rnq\nHObuahmgoHMhquZjx2iNUVFd86gB+rk/1bx5M7VBCQR0jfb2rudYlly5Hn889LySEkpZKxTk1y0W\nU7DWaHqfdjVjRtd32ZOhTKjiuejgAzPPsFmevhy59bkw2U2IlEfCy3pR31GPibETw06Pupjxsl60\nOdoCqXuO43Cy+WRgJvOh2kMhwysYhoGAEcDsMI9KYN5StgUbSzYiRZsCkUAEL+vFd2e/g5ARYm2l\nErBaKRDY7XSCTgd8+y312CpHoLjNr5b9gV2rvfBUc0cH8PbbwLx5pJAPHOhqkTpwoG/V3NhIbVUz\nZ9LIzGefpYEW3ZGG8ar3T7hSqYDycgrKRiPdyERH0x50ONWsUoWOn+S5rOADM8+wSdYk48l5T+I/\np/+DyrZKiIQiLB27FD8a96NhpW5ZjkV1O41VTFAnhFR4e3weNNuaIRPJEK0Iv+9W31GPbWe3odhY\njFhlLJanL8dE/cRBr4XjOByoPYDPTn8Gu4cC3MKUhRAJRNhWvg1qqRoSoQRNtiY0Whtx9dirAy1c\nHMeB47h+bUiHgsfnwbdl3wbmQAOUmk/WJGNX5S6saZkBaU/1pVBQoDYahx+Ym5upR1goDN1b3bz5\nwgnMO3eSsj1wgNLEIlHXjGaJpG/V7LfDlMvp5qOgYGDp5NJSUsypqdQipVaTKUlJSVfxmEJB6+GV\nME83eOcvnhGD4zg4vA6IBWKIheJhXavV1op1eetQY6kJuH5dk3ENbhp/EwSMAIdqD+Hfp/4Nu8cO\nFizGR4/HXVfcFeSFXd1ejef3Pw9wVN1t89jQ4erAHVPvGLSv9ZGGI3gz900YVAYoxAp4WS9KTaWo\nsdRg2ZhlAZ9to92Ib8u/xcSYiZhqmAov60VdRx1mJczCA7N6Gc4wRHysD7n1uXhm9zNI0iTBoDIE\nDeiotdTihateCDh+jQpuNwWg3gY8+NVzX61Bo01HB80vjomhffWqKuDKK8n56/BhYO5c2s998cVQ\n1dzYCDz1FLVGCYX0eVtbgVdfDVXNPXnrLVLaPdWv00nGJPw85fPCxRAbeMXMM2IwDDMiIyZZjsW6\nvHVosjYFDEZ8rA+bSzYjThUHvVKPvxb8FbGqWEQposBxHEpNpXjz8Jt4ZvEzgVnEGwo3QCQQBZSq\nXCyHWqLGJ6c/QU5SzqB8sL8u+RqR8sjA5xMJRFCJVTDajXB6nYGe6WhFNOYlzkNFWwVqLDUQC8W4\nJv0arBk3tLar3rC5bXjj8BsoNZWivqMejdZGKMQK5CTmQCfXweV1QSwUQyvTjuj7hiCRABP7yEC8\n/z6p1NdfpyB9Pti5k/Z5JRJSzU1NdDNx5gxVS+flkdHI0aPAihXB5/rVsj/VLJHQtXbt6l81r1nT\nuyXn+R6KwXNBwwdmnguO6vZq1FhqgsYmCgVC6JV6bC3fCr1CD5VEFbY9q8xUhqzoLLAcizMtZ0JG\nL0pFUnhYcigbzHjE+o76oGEa/jUxYGD32IPMTDQyDXKScrBs7DLoZDpMMUwJUrIjweayzSg1lyJV\nmwoGDI42HYWX9aKgoQA5STlotjZj7aS1IY5o5xR/5TPHAdu3AzedhznRHR3Uo+1XwhkZtB8cFUXp\n7ORkUtEPPADMmhV8rsUCHDlCwbu2tuu4z0d9xjfc0Hdx20gPx+C5bOADM88Fh81jA8MwIfvTcrEc\nRrsRHp8nfHsWw6DdSdWyDBjIxXK4fe6g4OTf7x3s1KgkTRLane1BCjRKEQUBIwhKi7l9bhyqOwS9\nQo9WeysA8qZ+NOfRkMA+VDiOw57KPUhQJ4BhGKRoqTWs2FiMFnsLjHYjbp96O65Mu3JE3m/I+Cuf\n9Xpg2zZSj+daNe/cSRXUkm43RlIprW3xYvo5JoZ+nz07+NyICBrwEc4IRCy+sCrOeS4peOcvnguO\nBHUCOI4LcQ0z2o2YoJ+AjKiMQAD2w3EcWI4N7KcyDINlY5ahobMhKHA2W5uRpksb9DjMVZmrYHaY\nA+5gfmevmyfcDB/nQ1VbFSrbKnG47jAkQglmxM9AqjYVqdpUuLwuvJP/Dlgu1PZzqHhYTyBl7w/O\nV4+9GnMT5+Kp+U9h6ZilgefPC361bDB0tQht3z667+nxhB5TqYCrrqJUtf8REUGV6f7hElotKeJT\np4LPZRgK2gZD6KN7LzMPzwjDK2aeCw6dXIdrMq7B5pLN0Cv1kIlkMDlM8LE+rMpaBQDIq88Lbs/q\nrMck/aTA1CkAWJGxAnUddTjSeITUNwfEqeNw34z7BlQt7vF5sO3sNmwr3wabx4ZIWSTanG0wOUwQ\nCoRYnr4cPxr3I3hYD063nIbL68IHxz5ArCoWgm49wTHKmEB6fiTaxxiGweyE2citzw14hgOAw+uA\nUqJEmi5t2O8xbPxq2a8qDYbRUc319dR+NHky8NxzwC9+QVXQfpYtC97n9XqBJ5+klLbLRU5c/uMb\nNlDgHmo/Nw/PCMEHZp4LkpvG34Q4VRy2lm+F2WHGBP0ErMpaFdgz7t6eJRaKsXzscqzOXh0UcKUi\nKR6c9SBqO2rR2NmICGkEMqMyAxXUfcFxHN4/9j6+r/0e8ap46OQ6tNpawYDBYzmPYUzkmEA6XAop\n5iTOAQB8dPKjsPvJDMPA5XWFHB8qq7NX40zrGVS3VyNCGgG7xw4v68X9M+8f8f3sQdPcTCnkiAiq\nYPZjsYzcXrPdToYd//kPUFwMXH89cPo0DY146KHez/N6qYXL6Qx9TqGg4rDRCMweDw34WL06fM8z\nD083+HYpnosWjuPg9DohFooDPbwjRX1HPX6383dUXNUt2Dd0NGBO0hzcOe3OsOe9m/8ujjcdD0qV\nu7wumBwmvHnNmwFHsJHA4rTgQM0BFBmLEKuMxaLURSHFbueF+noyMAn3/19qau+VyoPhk0+AHTu6\nLDCNRlK7LS3AH/4QrJovBL7/HnjlFeDXvwaWDK5Vj2dkuRhiA6+YeS5aGIYZ0UDXnfrOeggYQVBQ\n9rJeuH1ubD+7HUtSl4QEbQC4MftGnGk5g1pLLXRyHeweO2xuG+6YeseIr1Uj0+C6zOtwXeZ1I3rd\nYZOQANx99/Cvs2MHqdue5httbaS8jx2jACwWAxUVNG9ZJutfNZ9rPB6yKI2LI9U8dy6vmnn6hN9M\n4eEJQ4Q0AugWcy1OC3ZU7EBufS5KzaX4494/Yl3eOrh9wWMD49Rx+MPiP2DZ2GVQiBUYFz0Ov53/\nWyxOW3xuP8DFTl0d9UBv2RL63LZt1AbldNLrSkpoL7uqCoiNBY4fp58vFPLyALOZqtM7O8lXm4en\nD3jFzMMThozIDBiUBjRZmxCjiEFufS48Pg/EQjFmxs9ErDIW+Q35GHN2TIhijVHG4JaJt5ynlV8i\nbNpEVdObNtGwDf+oQ79aNpvJTtRkoiCtVJJpSGrqhaWa/Wo5JoZ+1+t51czTL7xi5uEJg1AgxMM5\nD8OgMqDIWBToSZ4SOwWxylgwDAODyoCdlTvP80ovQerqgNxcSlGfPEmDHvz41XJTExVqtbVRUZnZ\nTMfy88k288yZrorr84lfLfttORWKkVHNF/geKc/w4BUzz2WL0+vEqeZTaLW1Ij4iHhNiJgR5fOuV\nejyz6BnsqtyFd/PfRXZ0dtDzYoEYHa6O87H0S5tNmygoFxVRL/EnnwA/+Qmlq7dtox7ksWPpObud\njickAGPGAHfeSb7cAkGXSj1feL2klp3O4NnXTidNnZo3L9j4ZDDXffNN4JZbeGvPSxQ+MPNcljRZ\nm/Dy9y/D5DDRkAyWRaImEY/NfSzI3YthGMxJnINPTn8CDsEqpcXWgtmJs3temmc4+NWyVErKOCaG\nlPBnnwGLFlEhWFERqeWEBJraNGYM2V+qVEB2du/jG881DAOsWkUKvidiMT0/FAoKgP37SX3fd9/w\n1shzQcK3S/GcEziOg5f1QiQQDWsU5Eit5YX9L6Cuoy5o8lKtpRazE2fjl9N/GXLOjood+PDEh1BJ\nVJCL5Ghz0kzmpxc8PWLTm1iORUVbBVpsLYiURyIjMmNAPddDxeK04GzbWYgEImRGZQ7aprRPjh+n\nMZArVw7uvL/8hfaQKyspoEmlNE86Jgb4+mtSwo8/Tqnc9HR6D38gbmkB0tKAxx4betC70PF6gd/+\nlmxC29rIVIX35B4UF0Ns4BUzz6hzuvk0NhRuQLWlGiqJCtemX4vl6ctHvPd4oJgcJpSby0N6fuPV\n8city8XtU24PGf5wVdpViFfHY2fFThgdRsxJnIMr065ElGJkrBltbhveznsbJcaSwLFkTTIemvNQ\nyBzqkWD72e2BLADHcZCL5Xhg5gOYoJ8w/It7vcDHH1Nh1vz5g5s17G9/UqupUIphKAhZLBSIjhyh\n19lsNJxi4cIuK06tlvakz569dEcqFhRQz3ZqKn0HmzbxqvkShA/MPKNKsbEYrxx6BRqpBimaFDi9\nTnx65lO0Odvws8k/C7yO5ViUm8vRYmuBVqZFdnT2qAVuj88TdkiGf+6zjwsdWsAwDMbHjMf4mPGj\nsqZPz3yKEmMJkjXJgXXVd9Rj/fH1eDjn4RF9r1JTKT4++TESIhICLmFWtxVv5b6Fl69+mVrFhsPR\noxQ8/HvCa9cO/FyPh/qTJ06ktLSf+npyE8vPp37g6moK/M3NVIXtR6MhtX0pBmb/nrXfp9tgoLT/\nDTfwqvkSgw/MPKPKl0VfQi1RB1SfXCxHiiYFOyt24rqM66CT6wJqsdhYHDjPoDLgsbmPIVoRPeJr\nilXFIkoeBYvTAo1MEzjeam9FVnTWiMyUHgwurwsHaw8iISIh6GYhTh2Hk80nQ6ZaDZe91XshE8mC\nrDtVEpotfbLpJOanzB/6xb1e2g+OjKQ90B07gOXLB6aabTZqJVKpgPJySl/77TFlsi6bT5GIir+k\nUpoQ9bOf9XnZS4buahmg70Yi4VXzJQjfLsUzqpSby0NSsUKBEAJGgGZbMwDgi6IvUGIsQYomJTCR\nyeww43+P/u+o7AUJGAHumHoHOt2dqOuoQ7uzHbWWWnAch59O+umIv19/uH1u+FgfhEzwfrLfeczp\nDePrPAwsTkvY/WQGDKwe6/Au7lfL/gAKkGoeCLt3U+p65Upg2jTgtttoz/kvfwGef56uGRfX9XqD\nAdi1i97vcmDLlq7Z0P6Hx0Oq+UJoDeMZMXjFzDOq6JV62Nw2qKXqwDH/iEaNVAOPz4P9NftD1aIq\nDiXGEhjtRsQoR77tZbx+PJ5d8iz2Vu1FXUcdxqaMxcKUhUEK3e1z40TTCZSaShGtiMaM+Bkjtqfc\nHZVEhWRNMtqd7UE3MVa3FRHSCMQoRvbzT4mdgtMtp4Pey/9nMkY3ZugX7q6W/cTFDUw122zA5s1d\nhVwxMcCf/gS8/jqp47w8ajNqbAw+z+WiwHTdBWZLOhrcdVf44RsMQy1kPJcMfGDmGVWuz7we7xW8\nB6lIColQAo7jUNdRh4n6iTCoDHD5XPD6vCFq0b8H7PKN3ESmnsSr47F2Uvj9z05XJ145+AqqLdWQ\nCqXwsB58UfQFfjP7NyNTINUNhmHw00k/xZ8P/hlOrxMamQadrk44vA78atavRrwye27SXOys3Inq\n9mrEKGPgY31osbVgZsJMpEcOY2/25EmywoyIANq7zctubydl++Mf937u7t0UZP1uWBxH85Fffx14\n+21KWU+dGv5cjSb88UuNC20wB8+owbdL8YwqHMdhS9kWbCzZCB/rA8uxmGqYijun3Qm1VB1oXWq0\nNgapVZvbBrfPjVeXvzqgIjCL04Kq9ioUm4pRZiyDWCjGwpSFmJkwc0hFZJ+e+RRby7YiRds137nT\n1Qm3z43X/n97dx4dVXn3Afx7Z8u+ELJCVkLCkoQApgJCEJSAWkGUHg+24hZ73lfe054Kx+XtYrG+\nitqix7q0VIVSz7EoR1YLFApFkMUYtoJBCCaTDEkIJGRPJpnJPO8flwwZMpEkJHPvnfl+zpkDmS2/\nuZnMN797n/s8895wmWhksJQ3lOOf5/+JkvoSxIfEY97oeTcXlN+jsb0R/yr5Fw5ZDsFP74c7Uu7A\nzKSZN/e6amvlgVfuREcDib2sfNXWJk+f2dR0bcKNs2fl3bNd02umpQ28LqJutJANDGbyiFZbK6qb\nqxHiF9JjQFdJXQl+s/c3KG8oR4utBTpJh+igaLw460X8YOQPvvd569rq8N7X7+FA+QFUNlWixdaC\nmKAYZEZnos3ehtvib8N/5fwXdFL/hlP8z/b/QagptMdpU2UNZfjfGf+L9OHp/Xo++h42m3waVOfV\n0fA1NcDq1fKCFA0N8rzSy5d777nJ5FFayAYO/iKPCDQGImVYittR1v4Gf+gkHfQ6PfwN/gj1C4VR\nb0R5Q/n3PmdhZSEe3vgwPjz+Ic7Xnoel0QKTZILVZsV3V75DSlgKjlw4guLa4n7XK4RwOxGKBPX/\nUmuO0QhMnSpPUTl9ujzTV3w8kJQkr7F86lTvnTiRF2Iwk+K2nd2GAGMAZiXPwtzUuZiZNBNZ0Vn4\nR/E/UNdW5/YxdW11+HPhn1HTWoPY4FhIkgR/vT+uWK9AkiTUttWi2dYMg86AostF/a5pesJ0VDW5\nDjRq6WhBgCEAKcNSBvQ6qQ8qK4HDh68NApMk+bSrzZu5cAP5DAYzKe4/1f/p0Ul3HRe2NFrcPuZU\n9SnYO+2wOWww6AzOAVJ6nR6N7Y3ywDF7OxzCMaDzku9NvxexIbEw15txqeUSyhvKccV6BT+d/FOX\n83+9nhA9R0IPpe3b5cFi5eXyQDKzWV6o4vBh14Ug1EoI3zl9i4YMR2WT4sL8w9Bub+8ReAKi11Dt\nGq0dGRCJOmsdQv1CUWetgx562B12QABGvRGSXcKkuEkDqumFmS+gsLIQ39Z8i8jASExLmIbYYJUs\nkOApxcXyyOjf/tYzi0NMnQqk93L8Xgujr7/5Rj4+/tJL8hShRAPAYCbFzUudhw+OfYBAY6Cz873U\ncglxwXG9nlebNjwNQhJIj0zHwfKDMOgMiAyIREVTBQAg1C8UTe1NeGLSE4gOih5QXQHGAOQm5SI3\nKXdgL0zrhJBn4qqulie3yM8f+u+ZmTn032OoOBzylJmVlfIsZYsWKV0RaRSDmRQ3I3EGLI0W7CnZ\n47wuJjgGP5/y815HUyeFJWF20mzsKd2DrOgslNaXos5ah7SINCzOWIwpCVOQEZUxJAtA+IziYnmJ\nxYwM4Msv5Uk81LKkohoVFcmD1MaOBXbsAO68k10zDQhPlyLVuNxyGRcaLyDIFITUYak9JtZotbXi\nXO05OIQDaRFpCDIF4VjlMewv2482exumxk/FbQm3IcAYoNAr8CJCyDNvWSzyLFwVFfJuZk90zVrk\ncAC/+x1w5Yo8w5nFAtxzD7tmFdJCNrBjJtWICorqdfrNY1XHsLpwNTo65UXn9To9lkxYgtuTb0fO\nyBxPlukburrlrtmmYmPZNX+frm65+/Zi10wDxFHZpHo1rTV4r+A9hPmHISk8CUnhSYgKjMLaE2th\nrjff1HMLIWDrtKn+L2iP6jq2HBgo/9/hkE9b0unkY83kquvYcljYtUlQjEZ52+3Z8/2PJXKDHTOp\n3tHKo+gUnS4jtP0MfjDqjDhkOYTk8OR+P6cQAl9avsSWb7egprUG0YHRWDhuIabFT3M7sYhPaWoC\nLl+WZ+SqrLx2vSTJyzF2dsprLatBaam8PnHoTa4hfTNqa+Xt1dwMNDZeu14IeW1p7s6mfmIwU59Y\n7VYcuXAEBRUF8NP7ITcpFxNjJ/Z7qsuBaGpvcruQg0lvQoO1YUDPubd0L/564q+ICY5Bcngymtqb\n8Kev/wRbpw23J99+syVrW2gosGqV0lXcmNUKvPEGcOutwJIlytURFQW8845y35+8Dndl0w2129ux\n6tAqrDm+BhWNFSi+Uow3D7+Jj05+5HYXcGVTJfaX7cdhy2E0tje6ecb+GRM5xu3u5hZbC7Jisvr9\nfLZOGzae2YgRISMQbAoGAIT4hSAuJA6fnflMPg+a1O/wYaCuTl6ZSulJPSSp9wtRP7FjphsqqCjA\nudpzSAlPce7mjQiIwL/N/8bMpJnOKSqFEPi06FPsKN7hfKxRb8R/3/LfuGXELQP+/uOjxiMzOhOn\nLp1CdFA0JEi43HoZSWFJyBnR/4Ff9dZ6WO3WHgPNAo2BqGmtQWN7IyICvmftYG/icMjHjrXGagU+\n+wwYMULelbxjh7JdM9Eg0uBvJHlaYWUhQv1CXY696iQdJEg4W3vWed2pS6fw+dnPkRCagOTwZCSH\nJ2OY/zD8qfBPqLfWu3vqPtHr9Pj5lJ/j4ayH4af3g07S4YFxD+DZ6c/C3+Df7+cLNgVDJ+l6dMa2\nTnl6zyBj0IBr1RQh5Fm9Tp1SupL+O3xYPqYbGAjExamjayYaJOyY6YYCjAG97t7tPo3mF+YvEOoX\n6nI8ONAYiEstl3C6+jRmJM0YcA1+Bj/MHT0Xc0fPHfBzdAkwBuCOlDuw8/xOJIYlQq/To9PRCUuj\nBfem3+uy1KMQAla7FSa9ye1xbk07cwYoKJB3B2dkaKdz7uqWo6/O6GYwyLuM2TWTl2Aw0w3NSJyB\nQ+WH0OnodIaT1W6FTtJhQswE5/1aba3OxSeuZ+20eqTWvlo0fhE6OjvwRdkX8lKOEJgzag7uH3u/\n8z6FlYXY8M0GXGq5hCBTEO5JuwfzUud5R0ALAWzYIIebxSJ3zdnZSlfVN4cPy91xfLzcNQNASAiw\nezdw991AZM+lRYm0hMFMN5QRlYF7x9yL7cXbgavjrww6A/In57usCpUzIgd/O/k3l2kwHcIBAEgf\n3svCBAox6U14dOKjWDh2Ia60XUFEQATC/K8tknC08ijeOvIWooKikBSeBKvdir+f+juaO5rxYMaD\nClY+SM6cAUpK5AkxdDo5pLOytNE119fL015eLyJCPt7MYCaN45Sc1GcVjRU4V3sOBp0BGdEZPQZI\ntdna8OrBV2GuMyMiIAJ2hx311nrMGTUHj2Q/MijnBwshUG+th16nR6hf389dbbe341jVMZy8eBIh\nfiGYljCt1wUyhBD41d5foc3W5hLWdocdF5sv4s15byLEL+SmX4tihJCnj6ypkc8BFkJeXnHZMvV2\nzc3N8mQd99wjT95BNEBayAZ2zNRnI0NHYmToyF5vDzAG4Lnpz+Fg+UF8VfEVAgwBuD3pdkweMXlQ\nQrm0rhR/PfFXlDeUAwAyozPxSPYjvU7j2aXN1oZVh1ah+Eoxgk3B6OjswK7vduHhCQ8jLzWvx/3t\nDjuqmqqQGJbocr1BZ4AQAjWtNdoO5u7dMiAfnw0PV2/X7HAAy5fLdUdHA9OmKV0R0ZBiMNOgCjQG\nIi81z23g3Yya1hq8fvB1GHQGJIQm4FLrJWw9uxW7v9uNV+a88r2Tnewv249zV865dMgdnR34++m/\nI2dETo8VqAw6A8L9w9Fqa0WQ6doIbYdwwCEcLl20Ju3aJc/qVVFx7Toh5AUYiouBMWOUq82dkyeB\nL76QB3l98gmQk8Oumbwag5k04UDZAXR0diA6KBrHqo6hvLEcRp0RVc1V+PXeX2Nx5mIsmbDEbWd+\n0HLQ5Vg4IB9jFkLgXO05TImf4nKbJEmYnz4fa0+sRUJYAkx6ExzCgfKGckyLn6b9c5wffhhYuND9\nbSNGeLaWG3E45Fm1JEkO5m+/BQoL2TWTV2Mw05DqdHSiqaMJgcZAl1Or+qusoQxBpiBcbr2M8sZy\nhPuFQ5IkOIQDwaZg7CnZg6nxU90OMtPr9L0eU+qty56VMgvNtmZ8fu5zdDo6ISAwI3EGfpL1kwG/\nBtWIjNTOAKmTJ+VR2GFh8i726mrf7ZqF4ExiPoLBTEOia5GIz4o+Q2N7I4w6I/JG5eG+sffBqO//\nB2pSWBJOVZ9CbVstDJLB2Rk7hMM5COz4xeNug3lm4kysObHGZZKUNlsbDDoDxka6Gd0LObAXjFmA\nvFF5qGmtQahfqPZ3YWtNV7csBOB/dSKZ1tbeu2a7XV5EIkLjezTcsdvlecEfeghISFC6GhpiKhvl\nQd7iq4qv8JfCv8CoMyIxLBHDA4dj67mtWH96/YCeLzcpFwa9AS0dLRBCQAiBpna5E48NjgUEoIf7\n84tnJM7ALXG3wFxvxoXGCyirL0NNaw3yJ+ffcBBXgDEACWEJDGUl/Oc/cres18uB29gor2xVUgKs\nXy+HVXeffgo89RTQ0aFMvUOpsBA4dAjYvFnpSsgD2DHToBNCYPOZzYgOinYOnjLpTUgKS8K/zf/G\ngjEL+h10kYGReH7681h1eBV2nt+JTtGJmOAYZMdkQ4IEu7Bj8ojJbh9r1Bvxsw3/H/gAAA6lSURB\nVFt/hm9rvsWZy2cQbArG5BGTER0UfdOvlYbQl1/Ko7Cv330rBJCaKv/bpa0NeOsteVf3v/4ln1bl\nLex2eb3nUaPkgC4vBxITb/w40iyex0yDzu6w48mtT7pdJ9nSYMEvc3+J1IjUAT23w+HAmuNrsNe8\nFwGGAPn7CTsWpC/AA+Me4FrK3qStTb64Ex7uelrX2rXA//0fEBAgh9bmzYBp4GMaVOXIEeDPf5ZP\nb6uqAjIzgZ/9TOmqNEsL2cCOmQadXtIjMjASzR3NzmUVAfl4sIC4qVHNOp0O+ZPzMTtlNk5Wn4QE\nCRNjJyI5PJmh7G0CAuTLjbS1ycEVFgYEBcmnfHlL19zVLQ8fLn8dE8Ou2QfwGDMNOkmSsHDsQlQ3\nV8Nql+fItjvsKKsvw/SE6T3OGx7I86dGpOKBcQ/g/nH3I2VYCkPZl61fL89iFhIiH4/285MHjXnD\nsebCwmuvDZD3Evj7A1u2KFsXDSl2zDQkpidMh9VuxcYzG3G55TIkScKcUXO8Y55p6h+bTV4RKmQI\nZkvr3i13/XE2bJj3dM3bt8vb78KFa9c5HMDRo8DFi0BsrHK10ZDhMWYaUh2dHai31iPYFIxAY6DS\n5ZASNm6Up9P85S8H/zzcHTvkkdh6vetzt7cDc+bIx561zGKR/6i5niTJx5wN7K36SwvZwJ8qDSmT\n3sTRz76soUEOT6tVPv943LjBff7Jk4H333d/W1+7SYdD7kr9/G58X0/jOcs+SZMd84oVK/DBBx8g\nKkpevGDlypW46667XO6jhb+KiLzexo3A55/Lg7Kio4Ff/1p9s1dt3y7/0fD00+qrjQadFrJBkx2z\nJElYtmwZli1bpnQpRNSbrm45NlaePvP8+aHpmm9GSwuwdav8b0mJfH40kcI0Oypb7X/xEPm8PXvk\nmbpMJrkTDQ2VT/1R0+/uvn3y8eiQEGDTJnXVRj5Ls8H89ttvIzs7G/n5+aivr1e6HCLqrnu33CUi\n4lrXrAYtLcC2bfK5wVFRwKlTctdMpDDVBnNeXh6ysrJ6XLZu3YqnnnoKpaWlOHHiBOLi4rB8+XKl\nyyWi7s6elbvlqiqgrEy+lJfLtx09qmxtXbq6ZX9/uaMPCmLXTKqg2mPMu3fv7tP9nnzyScyfP9/t\nbStWrHD+f9asWZg1a9YgVEZEN/SDHwCTJrm/Te9+sRGP6t4td+neNfNYs9fYt28f9u3bp3QZ/aLJ\nUdlVVVWIi4sDALz55pv4+uuv8fHHH7vcRwsj74hIIV99Bbz7bs81ndvbgXnzgCVLlKmLhpwWskGT\nwfzII4/gxIkTkCQJKSkpWL16NWK6/+ULbWx8IlJIZ6e8jKQ7gYHqPKeZBoUWskGTwdwXWtj4RETk\nWVrIBtUO/iIiIvJFDGYiIiIVYTATERGpiGpPlyLSsqb2Jpy+dBpWuxWjho1CYlgi14wmoj5hMBMN\nstPVp/F2wdtot7cDV7N4RuIMPD7xceh1KjiHl4hUjcFMNIhaba145+t3EOIXgrgQ+Vx7h3DgC/MX\nGBc5DtMTpytcIRGpHY8xEw2iM5fPwGq3ItgU7LxOJ+kQERCBfeZ9yhVGRJrBYCYaRDaHDRJ6Hks2\n6o1otbcqUBERaQ2DmWgQpQ6T51i2O+wu19e21mLKyClKlEREGsNgJhpEUUFRmD9mPsrry3G55TIa\nrA0w15sRGxKL2cmzlS6PiDSAU3ISDTIhBE5fOo195n1obG9EzogcTE+c7nLcmYiUoYVsYDATEZHP\n0EI2cFc2ERGRijCYiYiIVITBTEREpCIMZiIiIhVhMBMREakIg5mIiEhFGMxEREQqwmAmIiJSEQYz\nERGRijCYiYiIVITBTEREpCIMZiIiIhVhMBMREakIg5mIiEhFGMxEREQqwmAmIiJSEQYzERGRijCY\niYiIVITBTEREpCIMZiJSTnU18OmngBBKV0KkGgxmIlLOtm3Ahg3Ad98pXQmRajCYiUgZVVXAl18C\nw4cDmzaxaya6isFMRMr4xz8AkwmIiQFOn2bXTHQVg5mIPK+rW46JASQJCApi10x0FYOZiDyvq1vW\n6+Wvo6LYNRNdxWAmIs+qrgb27AGsVsBsli9lZUBTE7Bli9LVESnOoHQBRORjAgOBpUvd3xYS4tla\niFRIEsI7D+pIkgQvfWlERDRAWsgG7somIiJSEQYzERGRijCYiYiIVITBTEREpCIMZiIiIhVhMBMR\nEakIg5mIiEhFGMxEREQqwmAmIiJSEQYzERGRijCYiYiIVITBTEREpCIMZiIiIhVhMBMREakIg5mI\niEhFGMxEREQqwmAmIiJSEQYzERGRijCYiYiIVITBTEREpCIMZiIiIhVhMBMREakIg5mIiEhFGMxE\nREQqwmAmIiJSEQYzERGRijCYiYiIVITBTEREpCIMZiIiIhVRbTBv2LABGRkZ0Ov1OHbsmMttK1eu\nRFpaGsaOHYtdu3YpVCEREdHgU20wZ2VlYdOmTZg5c6bL9UVFRfjkk09QVFSEnTt3YunSpXA4HApV\nqX779u1TugTFcRtwGwDcBgC3gVaoNpjHjh2L9PT0Htdv2bIFDz30EIxGI5KTkzF69GgUFBQoUKE2\n8BeR2wDgNgC4DQBuA61QbTD3prKyEvHx8c6v4+PjUVFRoWBFREREg8eg5DfPy8vDxYsXe1z/yiuv\nYP78+X1+HkmSBrMsIiIi5QiVmzVrljh69Kjz65UrV4qVK1c6v543b544cuRIj8elpqYKALzwwgsv\nvPDivKSmpnoku26Goh1zXwkhnP9fsGABfvzjH2PZsmWoqKhAcXExbr311h6POX/+vCdLJCIiGhSq\nPca8adMmJCQk4MiRI/jhD3+Iu+++GwAwfvx4PPjggxg/fjzuvvtuvPfee9yVTUREXkMS3dtRIiIi\nUpRqO+aB4sQkrlasWIH4+HhMmjQJkyZNws6dO5UuyWN27tyJsWPHIi0tDa+99prS5SgiOTkZEyZM\nwKRJk9we8vFGTzzxBGJiYpCVleW87sqVK8jLy0N6ejrmzp2L+vp6BSv0DHfbwZc+DywWC2bPno2M\njAxkZmbij3/8IwCNvBcUPsY96M6cOSPOnj3bY9DYN998I7Kzs0VHR4coLS0VqamporOzU8FKPWPF\nihVi1apVSpfhcXa7XaSmporS0lLR0dEhsrOzRVFRkdJleVxycrKora1VugyP2r9/vzh27JjIzMx0\nXvfMM8+I1157TQghxKuvviqee+45pcrzGHfbwZc+D6qqqsTx48eFEEI0NTWJ9PR0UVRUpIn3gtd1\nzJyYpCfhg0crCgoKMHr0aCQnJ8NoNGLx4sXYsmWL0mUpwtd+/rm5uRg2bJjLdVu3bsWjjz4KAHj0\n0UexefNmJUrzKHfbAfCd90NsbCwmTpwIAAgODsa4ceNQUVGhifeC1wVzb3x5YpK3334b2dnZyM/P\nV+dumyFQUVGBhIQE59e+9PPuTpIkzJkzBzk5OXj//feVLkcx1dXViImJAQDExMSgurpa4YqU44uf\nB2azGcePH8eUKVM08V7QZDDn5eUhKyurx2Xbtm39eh5vGc3d2/bYunUrnnrqKZSWluLEiROIi4vD\n8uXLlS7XI7zlZ3uzDh48iOPHj2PHjh149913ceDAAaVLUpwkST77/vDFz4Pm5mYsWrQIb731FkJC\nQlxuU+t7QRPnMV9v9+7d/X7MyJEjYbFYnF9fuHABI0eOHMyyFNPX7fHkk0/2a0Y1Lbv+522xWFz2\nmPiKuLg4AEBUVBTuv/9+FBQUIDc3V+GqPC8mJgYXL15EbGwsqqqqEB0drXRJiuj+un3h88Bms2HR\nokVYsmQJFi5cCEAb7wVNdsx9Ja6bmGT9+vXo6OhAaWlprxOTeJuqqirn/zdt2uQyQtOb5eTkoLi4\nGGazGR0dHfjkk0+wYMECpcvyqNbWVjQ1NQEAWlpasGvXLp/5+V9vwYIFWLduHQBg3bp1zg9pX+NL\nnwdCCOTn52P8+PH4xS9+4bxeE+8FRYeeDYGNGzeK+Ph44e/vL2JiYsRdd93lvO3ll18WqampYsyY\nMWLnzp0KVuk5S5YsEVlZWWLChAnivvvuExcvXlS6JI/Zvn27SE9PF6mpqeKVV15RuhyPKykpEdnZ\n2SI7O1tkZGT4zDZYvHixiIuLE0ajUcTHx4s1a9aI2tpaceedd4q0tDSRl5cn6urqlC5zyF2/HT78\n8EOf+jw4cOCAkCRJZGdni4kTJ4qJEyeKHTt2aOK9wAlGiIiIVMSrd2UTERFpDYOZiIhIRRjMRERE\nKsJgJiIiUhEGMxERkYowmImIiFSEwUxERKQiDGYiIiIV0eRc2UTU09GjR/HRRx9Br9fDbDbjgw8+\nwOrVq1FfX4+Kigq8+OKLGDVqlNJlEtENMJiJvEBJSQnWrl2Ld955BwDw2GOPYerUqVi3bh0cDgdy\nc3MxefJkPP300wpXSkQ3wmAm8gKrVq3C66+/7vy6paUFERERmDp1Ki5cuIDly5fjscceU65AIuoz\nzpVN5AXMZjOSk5OdX8fHx+Pxxx/HSy+9pFxRRDQgHPxF5AW6h/LZs2dRWVmJ2bNnK1cQEQ0Yg5nI\ny+zduxcmkwm33Xab87qSkhKX+zQ1NeFHP/oRLBaLp8sjohtgMBNpXFtbG5599lmcPn0aALB7925k\nZ2fD398fAOBwOPD73//eef8PP/wQb7zxBjZu3AgeySJSHw7+ItK47du34w9/+ANuueUWGAwGnD9/\nHuHh4c7bX375ZZeBX/n5+QCAF1980dOlElEfcPAXkcbV1tbimWeeQWRkJHQ6HV544QUsXboU/v7+\nMJlMuO+++3DnnXf2eJxOp4PZbEZiYqICVRNRbxjMRD6KwUykTjzGTEREpCIMZiIiIhVhMBP5MB7J\nIlIfBjORj/n444+xdOlSSJKE559/Hu+++67SJRFRNxz8RUREpCLsmImIiFSEwUxERKQiDGYiIiIV\nYTATERGpCIOZiIhIRRjMREREKsJgJiIiUhEGMxERkYowmImIiFTk/wEcZ8QlSSRnPwAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105728fd0>"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='mle_varying'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## MLE error rate for varying training set sizes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The MLE of the parameter $\\pmb\\mu$ is given by the equation:  \n",
      "${\\hat{\\pmb\\mu}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} \\pmb x_k$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu_estimates = {}\n",
      "\n",
      "for set_size in sorted(train_subsets):\n",
      "    mu1_est = np.sum(train_subsets[set_size]\\\n",
      "                     [train_subsets[set_size][:,2] == 1], axis=0)/\\\n",
      "        len(train_subsets[set_size]\\\n",
      "            [train_subsets[set_size][:,2] == 1])\n",
      "    mu1_est = mu1_est[0:2].reshape(2,1)\n",
      "    mu2_est = np.sum(train_subsets[set_size]\\\n",
      "                     [train_subsets[set_size][:,2] == 2], axis=0)/\\\n",
      "        len(train_subsets[set_size]\\\n",
      "            [train_subsets[set_size][:,2] == 2])\n",
      "    mu2_est = mu2_est[0:2].reshape(2,1)\n",
      "    mu3_est = np.sum(train_subsets[set_size]\\\n",
      "                     [train_subsets[set_size][:,2] == 3], axis=0)/\\\n",
      "        len(train_subsets[set_size]\\\n",
      "            [train_subsets[set_size][:,2] == 3])\n",
      "    mu3_est = mu3_est[0:2].reshape(2,1)\n",
      "    \n",
      "    mu_estimates[set_size] = [mu1_est, mu2_est, mu3_est]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The MLE of the parameter $\\pmb\\Sigma$ is given by the equation:  \n",
      "${\\hat{\\pmb\\Sigma}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} (\\pmb x_k - \\hat{\\mu})(\\pmb x_k - \\hat{\\mu})^t$\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mle_est_cov(x_samples, mu_est):\n",
      "    \"\"\"\n",
      "    Calculates the Maximum Likelihood Estimate for the covariance matrix.\n",
      "    \n",
      "    Keyword Arguments:\n",
      "        x_samples: np.array of the samples for 1 class, n x d dimensional \n",
      "        mu_est: np.array of the mean MLE, d x 1 dimensional\n",
      "        \n",
      "    Returns the MLE for the covariance matrix as d x d numpy array.\n",
      "    \n",
      "    \"\"\"\n",
      "    cov_est = np.zeros((2,2))\n",
      "    for x_vec in x_samples:\n",
      "        x_vec = x_vec.reshape(2,1)\n",
      "        assert(x_vec.shape == mu_est.shape),\\\n",
      "        'mean and x vector hmust be of equal shape'\n",
      "        cov_est += (x_vec - mu_est).dot((x_vec - mu_est).T)\n",
      "    return cov_est / len(x_samples)\n",
      "\n",
      "\n",
      "cov_estimates = {}\n",
      "\n",
      "for set_size in sorted(train_subsets):    \n",
      "    cov1_est = mle_est_cov(train_subsets[set_size]\\\n",
      "        [train_subsets[set_size][:,2] == 1][:,0:2], \n",
      "        mu_estimates[set_size][0])       \n",
      "    cov2_est = mle_est_cov(train_subsets[set_size]\\\n",
      "        [train_subsets[set_size]\\\n",
      "        [:,2] == 2][:,0:2], mu_estimates[set_size][1])   \n",
      "    cov3_est = mle_est_cov(train_subsets[set_size]\\\n",
      "        [train_subsets[set_size][:,2] == 3][:,0:2], \n",
      "        mu_estimates[set_size][2])\n",
      "    \n",
      "    cov_estimates[set_size] = [cov1_est, cov2_est, cov3_est]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def discriminant_function(x_vec, cov_mat, mu_vec):\n",
      "    \"\"\"\n",
      "    Calculates the value of the discriminant function for a dx1 dimensional\n",
      "    sample given the covariance matrix and mean vector.\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        cov_mat: numpy array of the covariance matrix.\n",
      "        mu_vec: dx1 dimensional numpy array of the sample mean.\n",
      "    \n",
      "    Returns a float value as result of the discriminant function.\n",
      "    \n",
      "    \"\"\"\n",
      "    W_i = (-1/2) * np.linalg.inv(cov_mat)\n",
      "    assert(W_i.shape[0] > 1 and W_i.shape[1] > 1), 'W_i must be a matrix'\n",
      "    \n",
      "    w_i = np.linalg.inv(cov_mat).dot(mu_vec)\n",
      "    assert(w_i.shape[0] > 1 and w_i.shape[1] == 1), 'w_i must be a column vector'\n",
      "    \n",
      "    omega_i_p1 = (((-1/2) *\\\n",
      "                   (mu_vec).T).dot(np.linalg.inv(cov_mat))).dot(mu_vec)\n",
      "    omega_i_p2 = (-1/2) *\\\n",
      "    np.log(np.linalg.det(cov_mat))\n",
      "    omega_i = omega_i_p1 - omega_i_p2\n",
      "    assert(omega_i.shape == (1, 1)),\\\n",
      "        'omega_i must be a scalar'\n",
      "    \n",
      "    g = ((x_vec.T).dot(W_i)).dot(x_vec) + (w_i.T).dot(x_vec) + omega_i\n",
      "    return float(g)\n",
      "\n",
      "\n",
      "def classify_data(x_vec, g, mu_vecs, cov_mats):\n",
      "    \"\"\"\n",
      "    Classifies an input sample into 1 out of 3 classes determined by\n",
      "    maximizing the discriminant function g_i().\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        g: The discriminant function.\n",
      "        mu_vecs: A list of mean vectors as input for g.\n",
      "        cov_mats: A list of covariance matrices as input for g.\n",
      "    \n",
      "    Returns a tuple (g_i()_value, class label).\n",
      "    \n",
      "    \"\"\"\n",
      "    assert(len(mu_vecs) == len(cov_mats)),\\\n",
      "        'Number of mu_vecs and cov_mats must be equal.'\n",
      "    \n",
      "    g_vals = []\n",
      "    for m,c in zip(mu_vecs, cov_mats): \n",
      "        g_vals.append(g(x_vec, mu_vec=m, cov_mat=c))\n",
      "    \n",
      "    max_index, max_value = max(enumerate(g_vals), \n",
      "                               key=operator.itemgetter(1))\n",
      "    return (max_value, max_index + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mle_errors = []\n",
      "for i in range(10,101,10):\n",
      "    classification_dict, error =\\\n",
      "            empirical_error(test_set, [1,2,3], classify_data, \n",
      "            [discriminant_function,\n",
      "            mu_estimates[i],\n",
      "            cov_estimates[i]])\n",
      "    mle_errors.append(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='parzen_varying'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Error rate of kernel density estimaton \n",
      "## via Parzen-window for varying training sizes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import operator\n",
      "\n",
      "def bayes_kde_class(x_vec, kdes):\n",
      "    \"\"\"\n",
      "    Classifies an input sample into class w_j determined by\n",
      "    maximizing the class conditional probability for p(x|w_j).\n",
      "    \n",
      "    Keyword arguments:\n",
      "        x_vec: A dx1 dimensional numpy array representing the sample.\n",
      "        kdes: List of the gausssian_kde (kernel density) estimates\n",
      "      \n",
      "    Returns a tuple ( p(x|w_j)_value, class label ).\n",
      "    \n",
      "    \"\"\"\n",
      "    p_vals = []\n",
      "    for kde in kdes:\n",
      "        p_vals.append(kde.evaluate(x_vec))\n",
      "    max_index, max_value = max(enumerate(p_vals), key=operator.itemgetter(1))\n",
      "    return (max_value, max_index + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import kde\n",
      "kde_errors = []\n",
      "for i in range(10,101,10):\n",
      "    class1_kde = kde.gaussian_kde(train_subsets[i]\\\n",
      "                [train_subsets[i][:,2] == 1].T[0:2], bw_method=1.0)\n",
      "    class2_kde = kde.gaussian_kde(train_subsets[i]\\\n",
      "                [train_subsets[i][:,2] == 2].T[0:2], bw_method=1.0)\n",
      "    class3_kde = kde.gaussian_kde(train_subsets[i]\\\n",
      "                [train_subsets[i][:,2] == 3].T[0:2], bw_method=1.0)\n",
      "    classification_dict, error = empirical_error(test_set, \n",
      "            [1,2,3], bayes_kde_class, [[class1_kde, class2_kde, class3_kde]])\n",
      "    kde_errors.append(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='k_nearest_error'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "##Error rate of the k-nearest-neighbor technique for varying training sizes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='plot_error_func'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "### Plotting the error rate as a \n",
      "### function of the size of the training set for each of the 3 cases"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def one_nn_classify(train_set, data_set):\n",
      "    \"\"\"\n",
      "    Classifies points in a dataset based on the\n",
      "    nearest neighbor in the training dataset\n",
      "    (1-nearest neighbor technique).\n",
      "    \n",
      "    Keyword arguments:\n",
      "        train_set: training points as nx3-dimensional numpy array.\n",
      "            with class label in the last column\n",
      "        data_set: training points as nx2-dimensional numpy array.\n",
      "            \n",
      "    Returns a list of the predicted class labels.\n",
      "\n",
      "\"\"\"\n",
      "    class_labels = [np.argmin([np.inner(p-x, p-x)  \n",
      "                               for x in train_set[:,0:-1]])for p in data_set]\n",
      "    return [int(train_set[cl,-1]) for cl in class_labels]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "one_nn_errors = []\n",
      "for i in train_subsets.keys():\n",
      "    true_class = test_set[:,-1]\n",
      "    predicted_class = one_nn_classify(train_subsets[i], test_set[:,0:-1])   \n",
      "    error = sum([1 for pred,true in zip(predicted_class,true_class)  \n",
      "             if pred!=true])/len(test_set)\n",
      "    one_nn_errors.append(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = ['MLE', 'Parzen-window (h=1)', '1=Nearest Neighbor']\n",
      "f, ax = plt.subplots(figsize=(10, 8))\n",
      "for err in [mle_errors, kde_errors, one_nn_errors]:\n",
      "    plt.plot([len(train_set)*i for i in np.arange(0.1,1.1,0.1)], err, lw=2)\n",
      "#plt.ylim([0,0.2])\n",
      "plt.xlabel('training set size')\n",
      "plt.ylabel('Error rate on test data set')\n",
      "plt.title('Error rate for different techniques')\n",
      "ylim([0,0.12])\n",
      "plt.legend(labels, loc='lower right')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAH4CAYAAADKGNCLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFIYBjoiIiEhhGOCIiIiIFOb/AbxNr3rFLfFXAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107778ac8>"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name=\"discussion_1\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 1f) - Discussion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Discuss you results along the following lines:  \n",
      "\n",
      "i. Which classifier results in the best performance on the test set?  \n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1 f) i. Best Classifier"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which classifier results in the best performance on the test set?\n",
      "\n",
      "Both parametric methods (here MLE) and non-parametric methods  \n",
      "(here: Parzen-window and 1-nearest neighbor) have good convergence properties.   \n",
      "In practice, it depends on the fact if we know the underlying model of the  \n",
      "data distribution when we are about to choose a parametric vs. a non-parametric approach.  \n",
      "If the model of the distribution is incorrect, the parametric approach yields an more  \n",
      "inaccurate estimate of the parameters.  \n",
      "The non-parametric approaches do not require knowledge about the underlying  \n",
      "distributions, since the estimate the probability density for local regions,  \n",
      "not the whole range of the distribution.  \n",
      "Assuming that the model  for the Maximum Likelihood Estimate is correct  \n",
      "(here: multivariate Gaussian distribution) I would expect to observe the  \n",
      "largest error for the 1-nearest neighbor technique, since it is a suboptimal  \n",
      "technique, which can be bounded being 2-times larger than the Bayes error - due to  \n",
      "the fact that the \"nearest neighbor\" can be far away in regions  \n",
      "of sparsity (esp. in smaller datasets).  \n",
      "The performance of the Parzen-window approach largely depends on the chosen window size.  \n",
      "In order to improve this approach, we would test the classifier-performance for different  \n",
      "window width, which we haven't done here.  \n",
      "\n",
      "Indeed, we can observe the worst performance on the training dataset for the 1-nearest  \n",
      "neighbor technique. A relatively low error rate for the MLE and Parzen-window kernel  \n",
      "density estimation suggest that the underlying assumption of the distribution for the  \n",
      "parametric approach (MLE) is accurate, and the chosen window width for the Parzen-window  \n",
      "approach is appropriate (however, it doesn't imply that the classifier cannot improved by  \n",
      "choosing a different window width)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"1f_i\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"1f_ii\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1 f) ii. Performance Changes as a function of patterns"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "ii. Does the performance of a classifier change significantly  \n",
      "  depending upon the patterns used in the training set?  \n",
      "  Substantiate your answer with an actual experiment on this dataset."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I would assume that the performance of the classifier should not change significantly  \n",
      "for different patterns in large training datasets, because we assume that the samples are *i.i.d*.  \n",
      "However, for small training dataset sizes, the performance of the clasifier can change  \n",
      "significantly because the estimates become more inaccurate (according to the principle of  \n",
      "convergence for large datasets) as we have seen in exercise 1 e). Reasons for such a significant  \n",
      "changes in the performance - especially for small datasets - can be the the local sparsity  \n",
      "of datapoints so that the MLE will be biased (because of a skew in the distribution)  \n",
      "and the non-parametric techniques cannot accurately predict the local denisties for certain points.\n",
      "\n",
      "An experiment to explore this hypothesis would be to draw different subsets of the same training  \n",
      "randomly multiple times and compare them against each other with respect to the error rates of the  \n",
      "different classifiers.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Drawing random training subsets again"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As in exercise 1 d), we will draw random training subsets from the  \n",
      "training dataset again with the sizes compared to the  \n",
      "original training dataset as follows: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%  \n",
      "of each class.  \n",
      "Then we will compute the MLE and estimates for the non-parametric techniques for  \n",
      "those new subsets and compare the error rates on the test dataset to the error rates\n",
      "from the the old training subsets."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This workaround is necessary to make sure that every subset \n",
      "# contains same number of\n",
      "# samples for each of the three classes\n",
      "\n",
      "train_subsets2_c1 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 1]\n",
      "    train_subsets2_c1[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "    \n",
      "train_subsets2_c2 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 2]\n",
      "    train_subsets2_c2[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "    \n",
      "train_subsets2_c3 = {}\n",
      "for p in reversed([i/10 for i in range(1,11)]):\n",
      "    t = train_set[train_set[:,2] == 3]\n",
      "    train_subsets2_c3[int(p*100)] =\\\n",
      "        t[np.random.choice(t.shape[0], t.shape[0] * p, replace=False),:]\n",
      "\n",
      "# combine separate-class subsets for convenience\n",
      "train_subsets2 = {}\n",
      "for set_size in sorted(train_subsets_c1.keys()):\n",
      "    t_set = np.zeros(shape=(1,3))\n",
      "    for subset in [train_subsets2_c1, train_subsets2_c2, train_subsets2_c3]:        \n",
      "        t_set = np.append(t_set, subset[set_size], axis=0)\n",
      "        # delete the first placeholder row used for initializing the array                                  \n",
      "    t_set = np.delete(t_set, 0, axis=0)\n",
      "    train_subsets2[set_size] = t_set\n",
      "\n",
      "for i in sorted(train_subsets2):\n",
      "    print(i, train_subsets2[i].shape)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 (105, 3)\n",
        "20 (210, 3)\n",
        "30 (315, 3)\n",
        "40 (420, 3)\n",
        "50 (525, 3)\n",
        "60 (630, 3)\n",
        "70 (732, 3)\n",
        "80 (840, 3)\n",
        "90 (945, 3)\n",
        "100 (1050, 3)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "one_nn_errors2 = []\n",
      "for i in train_subsets.keys():\n",
      "    true_class = test_set[:,-1]\n",
      "    predicted_class = one_nn_classify(train_subsets2[i], test_set[:,0:-1])   \n",
      "    error = sum([1 for pred,true in zip(predicted_class,true_class)  \n",
      "             if pred!=true])/len(test_set)\n",
      "    one_nn_errors2.append(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu_estimates2 = {}\n",
      "\n",
      "for set_size in sorted(train_subsets2):\n",
      "    mu1_est = np.sum(train_subsets2[set_size]\\\n",
      "                     [train_subsets2[set_size][:,2] == 1], axis=0)/\\\n",
      "        len(train_subsets2[set_size]\\\n",
      "            [train_subsets2[set_size][:,2] == 1])\n",
      "    mu1_est = mu1_est[0:2].reshape(2,1)\n",
      "    mu2_est = np.sum(train_subsets2[set_size]\\\n",
      "                     [train_subsets2[set_size][:,2] == 2], axis=0)/\\\n",
      "        len(train_subsets2[set_size]\\\n",
      "            [train_subsets2[set_size][:,2] == 2])\n",
      "    mu2_est = mu2_est[0:2].reshape(2,1)\n",
      "    mu3_est = np.sum(train_subsets2[set_size]\\\n",
      "                     [train_subsets2[set_size][:,2] == 3], axis=0)/\\\n",
      "        len(train_subsets2[set_size]\\\n",
      "            [train_subsets2[set_size][:,2] == 3])\n",
      "    mu3_est = mu3_est[0:2].reshape(2,1)\n",
      "    \n",
      "    mu_estimates2[set_size] = [mu1_est, mu2_est, mu3_est]\n",
      "\n",
      "cov_estimates2 = {}\n",
      "\n",
      "for set_size in sorted(train_subsets2):    \n",
      "    cov1_est = mle_est_cov(train_subsets2[set_size]\\\n",
      "        [train_subsets2[set_size][:,2] == 1][:,0:2], \n",
      "        mu_estimates2[set_size][0])       \n",
      "    cov2_est = mle_est_cov(train_subsets2[set_size]\\\n",
      "        [train_subsets2[set_size]\\\n",
      "        [:,2] == 2][:,0:2], mu_estimates2[set_size][1])   \n",
      "    cov3_est = mle_est_cov(train_subsets2[set_size]\\\n",
      "        [train_subsets2[set_size][:,2] == 3][:,0:2], \n",
      "        mu_estimates2[set_size][2])\n",
      "    \n",
      "    cov_estimates2[set_size] = [cov1_est, cov2_est, cov3_est]    \n",
      "    \n",
      "mle_errors2 = []\n",
      "for i in range(10,101,10):\n",
      "    classification_dict, error =\\\n",
      "            empirical_error(test_set, [1,2,3], classify_data, \n",
      "            [discriminant_function,\n",
      "            mu_estimates2[i],\n",
      "            cov_estimates[i]])\n",
      "    mle_errors2.append(error)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import kde\n",
      "\n",
      "kde_errors2 = []\n",
      "for i in range(10,101,10):\n",
      "    class1_kde = kde.gaussian_kde(train_subsets2[i]\\\n",
      "                [train_subsets[i][:,2] == 1].T[0:2], bw_method=1.0)\n",
      "    class2_kde = kde.gaussian_kde(train_subsets2[i]\\\n",
      "                [train_subsets[i][:,2] == 2].T[0:2], bw_method=1.0)\n",
      "    class3_kde = kde.gaussian_kde(train_subsets2[i]\\\n",
      "                [train_subsets[i][:,2] == 3].T[0:2], bw_method=1.0)\n",
      "    classification_dict, error = empirical_error(test_set, \n",
      "            [1,2,3], bayes_kde_class, [[class1_kde, class2_kde, class3_kde]])\n",
      "    kde_errors2.append(error)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def give_color():\n",
      "    for i in  ['red', 'blue', 'green']:\n",
      "        yield i\n",
      "colors = give_color()\n",
      "\n",
      "def give_labels():\n",
      "    for i in ['MLE tr_set1', 'MLE tr_set2',\n",
      "               'Parzen-wind. tr_set1', 'Parzen-wind. tr_set2',\n",
      "               '1-nn tr_set1', '1-nn tr_set2']:\n",
      "        yield i\n",
      "        \n",
      "colors = give_color()\n",
      "labels = give_labels()\n",
      "\n",
      "f, ax = plt.subplots(figsize=(10, 8))\n",
      "for err1, err2 in zip(\n",
      "        [mle_errors, kde_errors, one_nn_errors], \n",
      "        [mle_errors2, kde_errors2, one_nn_errors2]):\n",
      "    col = next(colors)\n",
      "    plt.plot([len(train_set)*i for i in np.arange(0.1,1.1,0.1)], \n",
      "             err1, color=col, lw=2, label=next(labels))\n",
      "    plt.plot([len(train_set)*i for i in np.arange(0.1,1.1,0.1)], \n",
      "             err2, color=col, linestyle='--', lw=2, label=next(labels))\n",
      "\n",
      "plt.xlabel('training set size')\n",
      "plt.ylabel('Error rate on test data set')\n",
      "plt.title('Error rate for different techniques')\n",
      "ylim([0,0.12])\n",
      "plt.legend(loc='lower right')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAH4CAYAAADKGNCLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10715f400>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Significant changes in the performance of the classifier can be observed throughout  \n",
      "the different sample sizes. As mentioned, this most likely occurs for small  \n",
      "training dataet sizes, here: n=300 (30% of the original training dataset size)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"1f_iii\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1 f) iii. Performance changes and the number of data patterns"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is expected that the performance for all 3 techniques  \n",
      "(MLE, Parzen-window kernel density estimation, 1-nearest neighbor) increase,  \n",
      "since all underly the assumption of convergence.  \n",
      "\n",
      "$\\lim\\limits_{n \\rightarrow \\infty}{p_n(\\pmb x)} = p(\\pmb x)$\n",
      "\n",
      "However, we don't see this effect so pronounced for our data set.  \n",
      "The error rate for all 3 approaches seems to be fluctuating around an average value.  \n",
      "Probably a wider range of different sample sizes would be necessary  \n",
      "to make this effect more obvious."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"1f_iv\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1 f) iv. Number of training patterns per class"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "iv. Do you think it is necessary for the number of training patterns per class to be the same?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is not necessary. However, if the prior probabilities are not equal,  \n",
      "we would have to normalize the weights for the observation for every class  \n",
      "in the Parzen-window technique and the 1-nearest neighbor technique.  \n",
      "For the MLE, we wouldn't be able to drop the prior probabilities from Bayes' rule,  \n",
      "which would lead to different discriminant functions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"task2\"></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 2. Thoughts on a Ray Kurzweil quote\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[10 points] In a New York Times article from 2003  \n",
      "(see http://writing.upenn.edu/~afilreis/88v/kurzweil.html), Ray Kurzweil   \n",
      "is quoted as saying \u201cThe real power of human thinking is  \n",
      "based on recognizing patterns. The better computers get at pattern recognition,  \n",
      "the more humanlike they will become.\u201d What are your thoughts on this statement?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "To assess this statement, one needs to know what exactly Ray Kurzweil  \n",
      "defines as \"human like\", however, if he only refers to the ability to recognize  \n",
      "patterns, I would agree that computers will definitely become more \"human-like\"  \n",
      "in terms of making \"appropriate\" decisions (or performing an specific action)  \n",
      "for a particular situation.  \n",
      "Limitations of pattern recognition for computers are the amount of available data  \n",
      "and the complexity of the data. The more powerful computers become, the more data  \n",
      "can be processed (given a infinite number of possible training samples) for learning  \n",
      "to distinguish more patterns. Also, more complex input data can be processed, and more  \n",
      "complex features can be incorporated into the classifier design. \n",
      "\n",
      "However, computers are still limited to distinguish patterns based on  \n",
      "numerical data - input data has to be processed into numbers. This may not be  \n",
      "applicable for all sorts of patterns. For example, for emotions on a human face  \n",
      "it may be challenging to break them down into numerical data.  \n",
      "Also, humans have the ability to make good decisions based on intuition,  \n",
      "where \"data\" or \"experience\" may not be available (i.e., for novel patterns  \n",
      "that are encountered for the first time). Since computers have to be \"pre-computed\"  \n",
      "and rely on data for the learning task, the recognition and choice of an appropriate  \n",
      "pattern might be very challenging (even if clever scientists develop recursive ways  \n",
      "so that a computer may learn novel patterns, it still requires previous data).\n",
      "\n",
      "Overall, computers are improving in pattern recognition tasks through recursive  \n",
      "self-learning algorithms, however the complexity of the humanlike behavior includes  \n",
      "more aspects than just the recognition of patterns. So, I agree that computer may become  \n",
      "\"more\" humanlike, but without becoming \"entirely\" human-like."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}